From 3268d16e210f88f880a3b16d70a4c1b8cf6defa0 Mon Sep 17 00:00:00 2001
From: Fedir <98510937+fMindstormer@users.noreply.github.com>
Date: Mon, 9 Nov 2026 21:08:53 -0500
Subject: [PATCH 01/11] Re-search: redraw a color whose region falls into parts

The greedy search takes one shape over a whole region (two circles
stacked on an oval become one tall box plus patches), because that shape
fixes the most pixels at its step. A new re-search neighborhood splits a
color's region where its distance to the edge comes apart, climbs one
shape per part against that part alone, refills, and keeps the result
only if the finished program scores better.

On cssynth's own scores (no Chromium yet), total J drops 4.6% on the
development split (9 targets better, none worse) and 3.4% on training
(4 better, none worse), for 5-8% more compile CPU. 2024-02-27 goes from
695 characters to 200. bench/evaluate.py gains a no-parts method.

Also: inset drops the lengths CSS repeats (inset:100px).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 README.md             | 17 +++++-----
 bench/evaluate.py     | 10 ++++--
 cssynth/codegen.py    | 16 ++++++++-
 cssynth/improve.py    | 46 ++++++++++++++++++++-----
 cssynth/search.py     | 79 +++++++++++++++++++++++++++++++++++++++++++
 docs/method.md        | 48 +++++++++++++++++++++-----
 tests/test_codegen.py | 10 +++++-
 tests/test_search.py  | 46 ++++++++++++++++++++++++-
 8 files changed, 242 insertions(+), 30 deletions(-)

diff --git a/README.md b/README.md
index 53c5e97..7d93f2f 100644
--- a/README.md
+++ b/README.md
@@ -321,14 +321,15 @@ tests/
   turns as motifs rescued some petals but lengthened other targets. A filter
   learned from outcome-labeled proposals didn't transfer to structures it
   hadn't seen (docs/method.md). Otherwise the search still goes one shape at
-  a time, and the re-search only repairs its choices locally. Two light circles stacked on an oval (2024-02-27) become one tall
-  rounded box plus patches, because the box fixes more pixels than either
-  circle, and a climb from one circle grows back into the box. The lotus is
-  drawn with polygons where a golfer uses three rotated leaves. Comparing
-  whole programs (two circles plus a cheap shadow against a box plus
-  patches) is the next step. A beam over partial J is not enough on its
-  own: the two-circle branch trails for two steps before it pulls ahead,
-  and the climbs never propose a lone circle to start it.
+  a time. The re-search now compares whole programs in one case: a color
+  whose region falls into parts (two circles stacked on an oval, a cloud of
+  discs) is redrawn with one shape per part, finished, and kept if the
+  finished program scores better. On cssynth's own scores this lowered the
+  development total by 4.6% and made no target worse (docs/method.md); it
+  hasn't been measured in Chromium yet. Choices that don't show up as parts
+  of one color still go one step at a time, and a beam over partial J
+  doesn't fix that: on 2024-02-27 the two-circle branch trails for two
+  steps before it pulls ahead.
 - **Reuse.** Turned copies of a box share a rule and mirrored copies of a
   polygon share a `clip-path`, but nothing yet uses `::before`/`::after` to
   repeat a shape, or mirrors a rounded box.
diff --git a/bench/evaluate.py b/bench/evaluate.py
index 4276ed8..9243dc4 100644
--- a/bench/evaluate.py
+++ b/bench/evaluate.py
@@ -30,7 +30,7 @@
 ROOT = Path(__file__).resolve().parent.parent
 sys.path.insert(0, str(ROOT))
 sys.path.insert(0, str(ROOT / "bench"))
-from cssynth import compiler, search, simplify  # noqa: E402
+from cssynth import compiler, improve, search, simplify  # noqa: E402
 
 BENCH = ROOT / "bench"
 FIDELITY = 99.0
@@ -67,6 +67,12 @@ def run(path):
     return run
 
 
+def _no_parts(path):
+    """The default compiler without re-searching colors in parts (v0.6.0)."""
+    improve.PARTS = False
+    return compiler.compile_image(path)
+
+
 def _fixes(path):
     """Everything but motifs."""
     search.MOTIFS = set()
@@ -96,7 +102,7 @@ def _learned(path):
 
 # name: path -> (code, info, model)
 METHODS = {"full": _full, "fast": _fast, "base": _base, "base-more": _base_more,
-           **{f"full-{n}": _full_more(n) for n in (16, 20)}, "fixes": _fixes,
+           **{f"full-{n}": _full_more(n) for n in (16, 20)}, "fixes": _fixes, "no-parts": _no_parts,
            "nest": _motifs(["nest"]), "nest-sym": _motifs(["nest", "symmetric"]),
            "all-motifs": _motifs(["nest", "symmetric", "seeded"]),
            "nest-allcolors": _motifs(["nest"], same_color=-1), "learned": _learned}
diff --git a/cssynth/codegen.py b/cssynth/codegen.py
index 77946b9..5e3aa22 100644
--- a/cssynth/codegen.py
+++ b/cssynth/codegen.py
@@ -403,10 +403,24 @@ def size_encodings(e: dict) -> list[dict]:
     x, y = float(e["left"]), float(e["top"])
     right, bottom = W - x - w, H - y - h
     d = {k: v for k, v in e.items() if k not in ("left", "top", "width", "height")}
-    out.append({"inset": " ".join(px(v) for v in (y, right, bottom, x)), **d})
+    out.append({"inset": sides(y, right, bottom, x), **d})
     return out
 
 
+def sides(top, right, bottom, left) -> str:
+    """Four lengths in CSS's shorthand order, dropping the ones it repeats:
+    a missing left is the right, a missing bottom the top, a missing right
+    the top."""
+    v = [top, right, bottom, left]
+    if left == right:
+        v.pop()
+        if bottom == top:
+            v.pop()
+            if right == top:
+                v.pop()
+    return " ".join(px(a) for a in v)
+
+
 def _insert_after(d: dict, key: str, item) -> dict:
     out = {}
     for k, v in d.items():
diff --git a/cssynth/improve.py b/cssynth/improve.py
index d3176f5..78fefab 100644
--- a/cssynth/improve.py
+++ b/cssynth/improve.py
@@ -11,17 +11,27 @@
   * one polygon, refilled without polygons for its color
   * every shape of one color, refilled as before
   * every shape of one color, refilled without polygons for that color
+  * every shape of one color whose region falls into parts (two circles
+    stacked, a cloud of discs), redrawn as one shape per part and refilled:
+    the greedy search takes the shape over the whole region, because it
+    fixes more pixels at that step than any one part, and patches the rest
 
 Each is tried once, in a single pass that continues from the improved stack.
+Every refill is finished (refined, simplified, emitted) before it is compared,
+so a choice that loses its step can win on the whole program.
 """
 
 from __future__ import annotations
 
-from .search import greedy, refine
+import numpy as np
+from scipy import ndimage
+
+from .search import add_parts, greedy, parts, refine
 from .shapes import Gradient
 from .simplify import simplify
 
 SCREEN = 1.3        # refills scoring worse than this times the best skip simplify
+PARTS = True        # redraw colors whose regions fall into parts (see split_colors)
 
 
 def _colors(pal, bg):
@@ -30,18 +40,35 @@ def _colors(pal, bg):
     return [c for c in range(len(pal)) if c != bg] + [bg]
 
 
-def neighborhoods(shapes):
-    """(key, shapes kept, colors to draw without polygons, description)."""
+def neighborhoods(shapes, split=None):
+    """(key, shapes kept, colors to draw without polygons, part masks to draw
+    first or None, description). split: {color: part masks} (see split_colors)."""
     for i, s in enumerate(shapes):
         if s.poly:
-            yield ("polygon", s.poly), shapes[:i] + shapes[i + 1:], (s.color,), f"polygon {i} without polygons"
+            yield ("polygon", s.poly), shapes[:i] + shapes[i + 1:], (s.color,), None, f"polygon {i} without polygons"
     colors = sorted({s.color for s in shapes})
     for c in colors:
-        yield ("color", c), [s for s in shapes if s.color != c], (), f"color {c}"
+        yield ("color", c), [s for s in shapes if s.color != c], (), None, f"color {c}"
     for c in colors:
         if any(s.poly for s in shapes if s.color == c):
-            yield (("color", c, "no polygons"), [s for s in shapes if s.color != c], (c,),
+            yield (("color", c, "no polygons"), [s for s in shapes if s.color != c], (c,), None,
                    f"color {c} without polygons")
+    for (c, whole), masks in (split or {}).items():
+        yield (("parts", c, whole), [s for s in shapes if s.color != c], (), masks,
+               f"color {c} in {len(masks)} {'whole ' if whole else ''}parts")
+
+
+def split_colors(T, colors):
+    """{(color, whole): masks of the parts of its regions} for the colors
+    with a region that falls into parts (see search.parts), both ways."""
+    out = {}
+    for c in colors:
+        lab, n = ndimage.label(T == c, structure=np.ones((3, 3)))
+        for whole in (False, True):
+            masks = [m for i in range(1, n + 1) for m in parts(lab == i, whole)]
+            if masks:
+                out[c, whole] = masks
+    return out
 
 
 def improve(score, pal, bg, T, shapes, rho, min_gain, per_color, bbox_starts, outlines,
@@ -50,13 +77,16 @@ def improve(score, pal, bg, T, shapes, rho, min_gain, per_color, bbox_starts, ou
     shapes by length as well as pixels (see search.greedy); motifs: propose
     motifs while refilling. Returns the improved stack."""
     best, best_J = shapes, score(shapes)
+    split = split_colors(T, [c for c in _colors(pal, bg) if c != bg]) if PARTS else {}
     tried = set()
     while True:
-        nb = next((nb for nb in neighborhoods(best) if nb[0] not in tried), None)
+        nb = next((nb for nb in neighborhoods(best, split) if nb[0] not in tried), None)
         if nb is None:
             return best
-        key, kept, no_polygons, what = nb
+        key, kept, no_polygons, seed, what = nb
         tried.add(key)
+        if seed is not None:
+            kept = add_parts(T, bg, kept, key[1], seed, min_gain)
         stack = greedy(T, bg, _colors(pal, bg), min_gain=min_gain, per_color=per_color,
                        bbox_starts=bbox_starts, outlines=outlines, start=kept,
                        no_polygons=no_polygons, pal=pal, rho=rho if by_length else None, motifs=motifs)[min_gain]
diff --git a/cssynth/search.py b/cssynth/search.py
index dbd333b..3eb754c 100644
--- a/cssynth/search.py
+++ b/cssynth/search.py
@@ -569,6 +569,85 @@ def tri_area(a, b, c):
     return poly if len(poly) == k else None
 
 
+PART_RISE = 3.0         # px a part's core must rise above the level where it splits off
+MIN_PART = 200          # pixels in a part
+
+
+def parts(region: np.ndarray, whole=False) -> list[np.ndarray]:
+    """The parts of a region that overlapping or touching shapes drew (two
+    circles stacked, a cloud of discs), as masks; [] when it doesn't split.
+    The region is thinned, by distance to its edge, until it falls apart;
+    each core counts if it rises PART_RISE above the level t where it split
+    off (so a bar's ragged ridge doesn't), and the lowest level with the most
+    cores is used. A part is what discs of radius t centered in its core
+    cover (the region opened at t, split between the cores), so a thinner
+    piece with no core of its own, like the bar under a cloud's discs, is
+    left to the search. whole: every pixel goes to the core nearest it
+    instead, which keeps thin ends (a leaf's tips) with their part."""
+    ys, xs = np.nonzero(region)
+    if len(xs) < 2 * MIN_PART:
+        return []
+    sl = (slice(ys.min(), ys.max() + 1), slice(xs.min(), xs.max() + 1))
+    r = np.pad(region[sl], 1)
+    D = ndimage.distance_transform_edt(r)
+    best = None
+    for t in np.arange(1.0, D.max()):
+        lab, n = ndimage.label(D > t)
+        if n < 2:
+            continue
+        idx = np.arange(1, n + 1)
+        keep = idx[ndimage.maximum(D, lab, idx) - t >= PART_RISE]
+        if len(keep) >= 2 and (best is None or len(keep) > len(best[0])):
+            best = keep, lab, t
+    if best is None:
+        return []
+    keep, lab, t = best
+    dist, (iy, ix) = ndimage.distance_transform_edt(~np.isin(lab, keep), return_indices=True)
+    owner = np.where(whole | (dist <= t), lab[iy, ix], 0)
+    out = []
+    for i in keep:
+        m = (r & (owner == i))[1:-1, 1:-1]
+        if m.sum() >= MIN_PART:
+            full = np.zeros_like(region)
+            full[sl] = m
+            out.append(full)
+    return out if len(out) >= 2 else []
+
+
+def add_parts(T, bg, shapes, c, masks, min_gain=MIN_GAIN):
+    """shapes with one shape of color c added per part mask, largest first,
+    each climbed on its own part's pixels only (so a disc stays a disc instead
+    of growing over its neighbors), at the depth where it fixes the most."""
+    shapes = list(shapes)
+    for p in sorted(masks, key=lambda m: -int(m.sum())):
+        belows, frees = _layers(T, bg, shapes)
+        ys, xs = np.nonzero(p)
+        x0, y0 = int(xs.min()), int(ys.min())
+        comp = p[y0:ys.max() + 1, x0:xs.max() + 1]
+        tables = []
+        for k in range(len(belows)):
+            A, B = _masks(T, belows[k], c, frees[k])
+            tables.append((k, A & p, B))
+        pool = []
+        for cand in candidates(c, comp, x0, y0):
+            cx, cy, m = coverage(cand)
+            if m is None:
+                continue
+            hh, ww = m.shape
+            g, k = max((np.count_nonzero(A[cy:cy + hh, cx:cx + ww] & m)
+                        - np.count_nonzero(B[cy:cy + hh, cx:cx + ww] & m), -k) for k, A, B in tables)
+            pool.append((g, -k, cand))
+        best = None
+        for _, k, cand in sorted(pool, key=lambda t: -t[0])[:TOP_STARTS]:
+            _, A, B = tables[k]
+            s, g = hill_climb(cand, A, B, sums=area_sums(A, B))
+            if best is None or g > best[0]:
+                best = (g, k, s)
+        if best is not None and best[0] >= min_gain:
+            shapes.insert(best[1], best[2])
+    return shapes
+
+
 def _masks(T, C, c, free=None):
     A = (T == c) & (C != c)
     B = (C == T) & (T != c)
diff --git a/docs/method.md b/docs/method.md
index 2657ec3..50c4653 100644
--- a/docs/method.md
+++ b/docs/method.md
@@ -148,6 +148,37 @@ polygon (and forbid polygons of its color), or every shape of one color
 remaining stack (in the winning variant's mode), refine, simplify, and
 keep the result if J drops.
 
+A third neighborhood redraws a color whose region falls into parts. The
+region is thinned by its distance to the edge until it comes apart; each
+piece whose core rises at least 3 px above that level is a part (the level
+with the most such cores wins). Two circles stacked on an oval give two
+parts; a cloud gives one per disc. Each part is either what discs of the
+split radius cover inside it (clean circles) or every pixel nearest its core
+(which keeps a leaf's tips), and both are tried. One shape is climbed per
+part, at its best depth, scored only against that part so that a disc
+can't grow over its neighbor; then the usual greedy refill takes over. The
+greedy search never makes this choice on its own: the shape over the whole
+region fixes more pixels at that step than any one part, and a climb from
+one circle grows back into it. Here the choice is judged on the finished
+program instead. Putting the same splits into the greedy search as
+candidates had raised the total J by 3% (see "What doesn't work yet").
+
+Scored with cssynth's own rasterizer, which needs no browser (the variants
+were run once per target, then re-searched both ways):
+
+| Split | Total J without parts | With parts | Characters | Targets better / worse | Re-search CPU |
+|---|---|---|---|---|---|
+| Development (66) | 60,046 | 57,305 (−4.6%) | 22,894 → 21,901 | 9 / 0 | +21% |
+| Training (48) | 72,966 | 70,491 (−3.4%) | 15,348 → 15,181 | 4 / 0 | +33% |
+
+The whole compile takes 5–8% more CPU. The two circles on the oval
+(2024-02-27) go from 695 characters to 200 (human: 481): a rounded box, a
+circle and its `box-shadow` copy. The cloud goes from 512 to 380, and the
+lotus from 551 to 484 (human: 418) with fewer wrong pixels. Three
+development targets (the oval, the cloud, 2024-03-30) become shorter than
+the human code, counting characters only; the match has yet to be checked
+in Chromium.
+
 ### Length-aware clean-up
 
 The search maximizes pixels fixed; the clean-up minimizes J. Its moves:
@@ -465,15 +496,14 @@ get rare cases right, not the average.
 
 ## What doesn't work yet
 
-- **Search order.** Re-search fixes some greedy mistakes, but a lotus of
-  three overlapping leaves is still drawn with polygons where a golfer uses
-  three rotated leaves, and two circles stacked on an oval become one tall
-  rounded box plus patches (the box fixes more pixels than either circle).
-  Two fixes were tried and dropped: proposing one shape over all of a
-  color's regions (the oval under the circles), and splitting regions at
-  narrow necks (into the two circles). Together they raised the total J by
-  3% and cost 9 targets their lead over the human code: the greedy search
-  takes the big shapes early and then patches around them.
+- **Search order.** Inside the greedy search, two fixes were tried and
+  dropped: proposing one shape over all of a color's regions (the oval
+  under the circles), and splitting regions at narrow necks (into the two
+  circles). Together they raised the total J by 3% and cost 9 targets their
+  lead over the human code: the greedy search takes the big shapes early
+  and then patches around them. The same splits now run in the re-search
+  (above), where the finished program decides. The lotus is still 66
+  characters longer than the human code, which uses three rotated leaves.
 - **More primitives:** gradients on shapes, borders, text.
 - **Antialiasing:** a linear ramp only approximates how Chromium smooths a
   rotated edge, so striped flags still carry a few hundred wrong pixels.
diff --git a/tests/test_codegen.py b/tests/test_codegen.py
index 8d524d6..3bce7ab 100644
--- a/tests/test_codegen.py
+++ b/tests/test_codegen.py
@@ -1,6 +1,6 @@
 import numpy as np
 
-from cssynth.codegen import bgcolor_attr, color_css, emit, gradient_css, num, px, radius_css, reused_clip
+from cssynth.codegen import bgcolor_attr, color_css, emit, gradient_css, num, px, radius_css, reused_clip, sides
 from cssynth.shapes import Gradient, Shape
 
 
@@ -66,3 +66,11 @@ def test_mirrored_polygon_reuses_the_clip_path():
         assert code.count("polygon(") == 1 and f"scale:{scale}" in code
     assert reused_clip(a, a.moved(5, 5)) == ""
     assert reused_clip(a, Shape.polygon([(10, 10), (60, 10), (41, 50), (20, 30)], 1)) is None
+
+
+def test_sides_drop_what_css_repeats():
+    assert sides(100, 100, 100, 100) == "100px"
+    assert sides(50, 100, 50, 100) == "50px 100px"
+    assert sides(50, 100, 70, 100) == "50px 100px 70px"
+    assert sides(50, 100, 50, 0) == "50px 100px 50px 0"
+    assert sides(0, 0, 0, 0) == "0"
diff --git a/tests/test_search.py b/tests/test_search.py
index 00c5289..e3b3d4e 100644
--- a/tests/test_search.py
+++ b/tests/test_search.py
@@ -3,15 +3,18 @@
 
 from cssynth.search import (
     _regions,
+    add_parts,
     area_sums,
     char_cost,
+    error,
     extract_palette,
     gain,
+    parts,
     reduce_polygon,
     soften,
     trace_outline,
 )
-from cssynth.shapes import Gradient, Shape, base_rgb, coverage
+from cssynth.shapes import Gradient, Shape, base_rgb, coverage, render_labels
 
 
 def test_trace_outline_of_concave_region_is_exact():
@@ -116,3 +119,44 @@ def test_char_cost_of_a_shifted_copy_is_a_shadow_entry():
     full = char_cost(a, [], pal)
     copy = char_cost(Shape(200, 40, 60, 60, 1), [a], pal)
     assert copy < full // 2
+
+
+def _disc(cx, cy, r):
+    yy, xx = np.mgrid[:300, :400] + 0.5
+    return (xx - cx) ** 2 + (yy - cy) ** 2 <= r * r
+
+
+def test_parts_split_touching_discs():
+    region = _disc(200, 100, 50) | _disc(200, 200, 50)
+    ps = parts(region)
+    assert len(ps) == 2
+    for p, disc in zip(sorted(ps, key=lambda m: np.nonzero(m)[0].mean()), (_disc(200, 100, 50), _disc(200, 200, 50))):
+        assert (p & ~disc).sum() == 0 and (disc & ~p).sum() < 0.03 * disc.sum()
+
+
+def test_parts_leave_a_bar_without_a_core_to_the_search():
+    bar = np.zeros((300, 400), dtype=bool)
+    bar[165:215, 150:300] = True
+    region = _disc(150, 165, 50) | _disc(230, 135, 50) | bar        # a cloud
+    ps = parts(region)
+    assert len(ps) == 2
+    left = (region & ~np.logical_or(*ps)).sum()
+    assert left > 0.5 * bar[:, 280:].sum()              # the bar's far end is in no part
+    assert np.logical_or(*parts(region, whole=True)).sum() == region.sum()
+
+
+def test_parts_of_one_shape_are_none():
+    bar = np.zeros((300, 400), dtype=bool)
+    bar[100:150, 50:350] = True
+    assert parts(_disc(200, 150, 80)) == [] and parts(bar) == []
+
+
+def test_add_parts_draws_each_disc_on_its_own():
+    # two circles stacked on a stadium: one shape over both circles fixes
+    # more pixels than either, so the greedy search takes it and patches
+    stadium = Shape(100, 100, 200, 100, 1, radii=(50,) * 4)
+    circles = [Shape(150, 50, 100, 100, 2, ell=True), Shape(150, 150, 100, 100, 2, ell=True)]
+    T = render_labels(0, [stadium] + circles)
+    shapes = add_parts(T, 0, [stadium], 2, parts(T == 2))
+    assert len(shapes) == 3 and error(T, 0, shapes) < 100
+    assert sorted((s.x, s.y, s.w, s.h) for s in shapes[1:]) == [(150, 50, 100, 100), (150, 150, 100, 100)]

From 7c86019a309521121fbc2a21a6f9800206939335 Mon Sep 17 00:00:00 2001
From: Fedir <98510937+fMindstormer@users.noreply.github.com>
Date: Mon, 9 Nov 2026 22:50:39 -0500
Subject: [PATCH 02/11] Option to re-search the N best results, not only the
 best

A result that starts a little worse sometimes re-searches to a much
better program. compile_image(research=N) and --research N re-search the
N best distinct results, in parallel when there are several processes,
and keep the best; 1 stays the default.

On cssynth's own scores, the 3 best lower total J by 2.3% on the
development split and 2.5% on training, for 1.5-2x the compile CPU and
little more wall time on 8 cores. The lotus drops from 484 characters to
291 (human: 418). bench/evaluate.py gains a top3 method.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 README.md             | 11 +++++---
 bench/evaluate.py     |  7 ++++-
 cssynth/__main__.py   |  5 +++-
 cssynth/compiler.py   | 61 +++++++++++++++++++++++++++++--------------
 docs/method.md        | 18 +++++++++++++
 tests/test_compile.py | 14 +++++++++-
 6 files changed, 89 insertions(+), 27 deletions(-)

diff --git a/README.md b/README.md
index 7d93f2f..65b80ce 100644
--- a/README.md
+++ b/README.md
@@ -124,6 +124,7 @@ cssynth target.png           # prints the code; length and details on stderr
 cssynth -v target.png        # also logs the search
 cssynth --rho 2 target.png   # golf harder (see "Objective")
 cssynth --fast target.png    # skip the re-search: about 2x faster, longer code
+cssynth --research 3 target.png  # re-search the 3 best results: about 2% lower J, more CPU
 ```
 
 From Python:
@@ -245,10 +246,12 @@ flowchart LR
    the order they first help, so an early polygon can take a region that a
    shorter primitive would have drawn. The best result is taken apart and
    refilled: one polygon at a time (refilled without polygons of its color),
-   then every shape of one color (refilled as before, and without polygons).
-   Refills use the winning variant's mode. Each is refined and simplified,
-   and kept when J drops. `--fast`
-   skips this step.
+   then every shape of one color (refilled as before, and without polygons),
+   then every shape of a color whose region falls into parts, one shape per
+   part (two circles stacked on an oval). Refills use the winning variant's
+   mode. Each is refined and simplified, and kept when J drops. `--fast`
+   skips this step; `--research 3` runs it on the 3 best results, in
+   parallel, and keeps the best.
 
 ## Benchmark
 
diff --git a/bench/evaluate.py b/bench/evaluate.py
index 9243dc4..80cf29c 100644
--- a/bench/evaluate.py
+++ b/bench/evaluate.py
@@ -73,6 +73,11 @@ def _no_parts(path):
     return compiler.compile_image(path)
 
 
+def _top3(path):
+    """The default compiler, re-searching the 3 best results."""
+    return compiler.compile_image(path, research=3)
+
+
 def _fixes(path):
     """Everything but motifs."""
     search.MOTIFS = set()
@@ -102,7 +107,7 @@ def _learned(path):
 
 # name: path -> (code, info, model)
 METHODS = {"full": _full, "fast": _fast, "base": _base, "base-more": _base_more,
-           **{f"full-{n}": _full_more(n) for n in (16, 20)}, "fixes": _fixes, "no-parts": _no_parts,
+           **{f"full-{n}": _full_more(n) for n in (16, 20)}, "fixes": _fixes, "no-parts": _no_parts, "top3": _top3,
            "nest": _motifs(["nest"]), "nest-sym": _motifs(["nest", "symmetric"]),
            "all-motifs": _motifs(["nest", "symmetric", "seeded"]),
            "nest-allcolors": _motifs(["nest"], same_color=-1), "learned": _learned}
diff --git a/cssynth/__main__.py b/cssynth/__main__.py
index 3972b15..acde41f 100644
--- a/cssynth/__main__.py
+++ b/cssynth/__main__.py
@@ -16,8 +16,11 @@ def main():
                     help="processes for the search variants (default: all cores)")
     ap.add_argument("--fast", action="store_true",
                     help="skip re-searching parts of the result (several times faster, a little longer code)")
+    ap.add_argument("--research", type=int, default=1, metavar="N",
+                    help="re-search the N best results, not only the best (default %(default)s; 3 lowered the "
+                         "benchmark's J by about 2%% for 1.5 to 2 times the CPU)")
     args = ap.parse_args()
-    code, info, _ = compile_image(args.image, rho=args.rho, research=not args.fast, jobs=args.jobs,
+    code, info, _ = compile_image(args.image, rho=args.rho, research=0 if args.fast else args.research, jobs=args.jobs,
                                   log=(lambda m: print(m, file=sys.stderr)) if args.v else None)
     print(code)
     print(f"{len(code)} chars, {info}", file=sys.stderr)
diff --git a/cssynth/compiler.py b/cssynth/compiler.py
index 28fc7b0..a002518 100644
--- a/cssynth/compiler.py
+++ b/cssynth/compiler.py
@@ -63,30 +63,51 @@ def _run_variant(arr, variant, rho, log=None):
     return runs
 
 
-def compile_image(image, log=None, variants=VARIANTS, rho=RHO, research=True, jobs=1):
-    """image: a path or a PIL image. research: re-search parts of the best
-    result (see improve). jobs: processes to run the variants in (the
-    result is the same for any number)."""
+def _research(arr, rho, run, log=None):
+    """Re-search one result (see improve); the result, improved or not."""
+    J, code, info, (pal, bg, T, shapes) = run
+    min_gain, per_color, bbox_starts, _, outlines, by_length, motifs = info["variant"]
+    better = improve(lambda sh: _finish(arr, rho, pal, bg, sh)[0], pal, bg, T, shapes, rho,
+                     min_gain, per_color, bbox_starts, outlines, by_length=by_length, motifs=motifs, log=log)
+    if better is shapes:
+        return run
+    J2, code, info2 = _finish(arr, rho, pal, bg, better)
+    return J2, code, {**info2, "variant": info["variant"], "researched": round(J - J2, 1)}, (pal, bg, T, better)
+
+
+def compile_image(image, log=None, variants=VARIANTS, rho=RHO, research=1, jobs=1):
+    """image: a path or a PIL image. research: how many of the best distinct
+    results to re-search (see improve; 0 or False: none); the best one after
+    re-searching wins. A result that isn't the best can re-search to a better
+    one, so more costs CPU and can only lower J. jobs: processes to run the
+    variants and re-searches in (the result is the same for any number)."""
     img = (image if isinstance(image, Image.Image) else Image.open(image)).convert("RGB")
     if img.size != (400, 300):
         img = img.resize((400, 300), Image.BOX)
     arr = np.asarray(img)
 
+    ex = None
     if jobs > 1:
         from concurrent.futures import ProcessPoolExecutor  # not in browsers (Pyodide)
-        with ProcessPoolExecutor(min(jobs, len(variants))) as ex:
-            per_variant = list(ex.map(_run_variant, *zip(*[(arr, v, rho) for v in variants])))
-    else:
-        per_variant = [_run_variant(arr, v, rho, log) for v in variants]
-    runs = [run for rs in per_variant for run in rs]
-    J, code, info, (pal, bg, T, shapes) = min(runs, key=lambda r: r[0])
-    if research:
-        min_gain, per_color, bbox_starts, _, outlines, by_length, motifs = info["variant"]
-        better = improve(lambda sh: _finish(arr, rho, pal, bg, sh)[0], pal, bg, T, shapes, rho,
-                         min_gain, per_color, bbox_starts, outlines, by_length=by_length, motifs=motifs,
-                         log=log)
-        if better is not shapes:
-            J2, code, info2 = _finish(arr, rho, pal, bg, better)
-            info = {**info2, "variant": info["variant"], "researched": round(J - J2, 1)}
-            shapes = better
-    return code, info, (pal, bg, T, shapes)
+        ex = ProcessPoolExecutor(min(jobs, len(variants)))
+
+    def each(f, args):           # in the pool, or here with the log
+        return list(ex.map(f, *zip(*args))) if ex and len(args) > 1 else [f(*a, log) for a in args]
+
+    try:
+        runs = sorted((run for rs in each(_run_variant, [(arr, v, rho) for v in variants]) for run in rs),
+                      key=lambda r: r[0])
+        picked, seen = [], set()
+        for run in runs:
+            if len(picked) >= int(research):
+                break
+            if run[1] not in seen:
+                seen.add(run[1])
+                picked.append(run)
+        if picked:
+            runs = each(_research, [(arr, rho, run) for run in picked])
+    finally:
+        if ex:
+            ex.shutdown()
+    J, code, info, model = min(runs, key=lambda r: r[0])
+    return code, info, model
diff --git a/docs/method.md b/docs/method.md
index 50c4653..e38ce5b 100644
--- a/docs/method.md
+++ b/docs/method.md
@@ -179,6 +179,24 @@ development targets (the oval, the cloud, 2024-03-30) become shorter than
 the human code, counting characters only; the match has yet to be checked
 in Chromium.
 
+Re-searching only the best result can miss a better program: a result that
+starts a little worse sometimes re-searches to a much better one. With
+`research=3`, the three best distinct results are re-searched (in parallel
+when there are several processes) and the best wins:
+
+| Split | Re-search the best | The 2 best | The 3 best | Re-search CPU (3 best) |
+|---|---|---|---|---|
+| Development (66) | 57,305 | 56,623 (−1.2%) | 55,994 (−2.3%) | 3.5× |
+| Training (48) | 70,284 | 70,222 (−0.1%) | 68,534 (−2.5%) | 5.8× |
+
+The whole compile takes 1.5 to 2 times the CPU, but little more wall time
+on 8 cores (the lotus: 16.9 s against 17.8 s). The lotus drops to 291
+characters (human: 418), from the second-best result. The winners come
+from many different variants, so re-searching one fixed extra variant
+wouldn't do the same. It isn't the default yet, since the published numbers
+are the default's, measured in Chromium. (The training figures here include
+the shorter `inset`, which the table above doesn't.)
+
 ### Length-aware clean-up
 
 The search maximizes pixels fixed; the clean-up minimizes J. Its moves:
diff --git a/tests/test_compile.py b/tests/test_compile.py
index b596e04..f54ec04 100644
--- a/tests/test_compile.py
+++ b/tests/test_compile.py
@@ -51,12 +51,24 @@ def test_leaf_is_one_turned_shape(tmp_path):
     assert len(code) < 140
 
 
-def test_parallel_variants_give_the_same_code(tmp_path):
+def disc_and_triangle(tmp_path):
     img = Image.new("RGB", (400, 300), "#62374e")
     d = ImageDraw.Draw(img)
     d.ellipse([100, 50, 199, 149], fill="#fdc57b")
     d.polygon([(250, 200), (350, 200), (300, 120)], fill="#fdc57b")
     path = tmp_path / "t.png"
     img.save(path)
+    return path
+
+
+def test_parallel_variants_give_the_same_code(tmp_path):
+    path = disc_and_triangle(tmp_path)
     serial = compile_image(path, research=False)[0]
     assert compile_image(path, research=False, jobs=3)[0] == serial
+
+
+def test_researching_more_results_never_scores_worse(tmp_path):
+    path = disc_and_triangle(tmp_path)
+    code, info, _ = compile_image(path, research=3, jobs=3)
+    assert info["J"] <= compile_image(path)[1]["J"]
+    assert compile_image(path, research=3)[0] == code

From 9d502216f5e89bec43a0aa311a4ff63774786cb1 Mon Sep 17 00:00:00 2001
From: Fedir <98510937+fMindstormer@users.noreply.github.com>
Date: Tue, 10 Nov 2026 09:26:30 -0500
Subject: [PATCH 03/11] Benchmark: an input for how many results to re-search

bench/run.py --research N, and the Benchmark workflow's research input
(default 1), so --research 3 can be measured in Chromium too.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .github/workflows/benchmark.yml |  6 +++++-
 bench/run.py                    | 10 ++++++----
 2 files changed, 11 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml
index 4f310ee..12cd154 100644
--- a/.github/workflows/benchmark.yml
+++ b/.github/workflows/benchmark.yml
@@ -4,6 +4,10 @@ name: Benchmark
 
 on:
   workflow_dispatch:
+    inputs:
+      research:
+        description: "Re-search the N best results (compile_image research=N)"
+        default: "1"
 
 jobs:
   bench:
@@ -21,7 +25,7 @@ jobs:
       - run: npm ci
         working-directory: bench
       - run: python bench/fetch.py
-      - run: python bench/run.py
+      - run: python bench/run.py --research ${{ inputs.research }}
       - run: python bench/tradeoff.py
       - run: python bench/build_page.py
       - uses: actions/upload-artifact@v7
diff --git a/bench/run.py b/bench/run.py
index c0a215d..495918a 100755
--- a/bench/run.py
+++ b/bench/run.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """Compile every target, render the output in Chromium, and score it.
 
-    python bench/run.py [--only id,id] [--jobs 8]
+    python bench/run.py [--only id,id] [--jobs 8] [--research 3]
 
 Writes bench/out/<id>.html, bench/out/<id>.png and bench/results.json.
 Match = share of pixels whose largest channel difference is at most 8/255,
@@ -15,6 +15,7 @@
 import subprocess
 import sys
 import time
+from functools import partial
 from multiprocessing import Pool
 from pathlib import Path
 
@@ -34,10 +35,10 @@ def match(a, b, tol=8):
     return float((np.abs(a.astype(int) - b.astype(int)).max(2) <= tol).mean() * 100)
 
 
-def work(case):
+def work(case, research=1):
     t = time.time()
     target = BENCH / "human" / f"{case['id']}.png"
-    code, info, (pal, bg, T, shapes) = compile_image(target)
+    code, info, (pal, bg, T, shapes) = compile_image(target, research=research)
     sim = render_rgb(bg, pal, shapes)
     img = np.asarray(Image.open(target).convert("RGB"))
     (OUT / f"{case['id']}.html").write_text(code)
@@ -52,6 +53,7 @@ def main():
     ap = argparse.ArgumentParser()
     ap.add_argument("--only")
     ap.add_argument("--jobs", type=int, default=os.cpu_count())
+    ap.add_argument("--research", type=int, default=1, help="re-search the N best results (see compile_image)")
     args = ap.parse_args()
     cases = json.loads((BENCH / "cases.json").read_text())
     if args.only:
@@ -59,7 +61,7 @@ def main():
         cases = [c for c in cases if c["id"] in keep]
     OUT.mkdir(exist_ok=True)
     with Pool(args.jobs) as p:
-        res = p.map(work, cases)
+        res = p.map(partial(work, research=args.research), cases)
 
     jobs = [{"html": r["code"], "out": str(OUT / f"{r['id']}.png")} for r in res]
     (BENCH / "jobs_out.json").write_text(json.dumps(jobs))

From 5c9983726287d884c450c452122462408cf9e63d Mon Sep 17 00:00:00 2001
From: Fedir <98510937+fMindstormer@users.noreply.github.com>
Date: Tue, 10 Nov 2026 10:36:00 -0500
Subject: [PATCH 04/11] Keep a shared padding when inset is shorter for one
 element; Chromium numbers

Since inset drops repeated lengths, it can be the shortest encoding for
one element and break a padding the elements would share (ks-04-upsndowns
went from 207 to 214 characters). Code generation now also tries every
element's shortest encoding without inset: about 55 characters shorter per
benchmark run, never longer.

The Benchmark workflow on this branch, against 0.6.0: total J 52,153 ->
48,985 (-6.1%), 23,329 -> 21,817 characters, 59 targets shorter than the
human code instead of 56, all 66 still at >= 99% match; re-searching the 3
best results, 47,179 (-9.5%) and 21,283 characters.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 README.md             |  5 +++--
 cssynth/codegen.py    | 11 +++++++++--
 docs/method.md        | 23 ++++++++++++++++++-----
 tests/test_codegen.py |  7 +++++++
 4 files changed, 37 insertions(+), 9 deletions(-)

diff --git a/README.md b/README.md
index 65b80ce..51db12d 100644
--- a/README.md
+++ b/README.md
@@ -328,8 +328,9 @@ tests/
   whose region falls into parts (two circles stacked on an oval, a cloud of
   discs) is redrawn with one shape per part, finished, and kept if the
   finished program scores better. On cssynth's own scores this lowered the
-  development total by 4.6% and made no target worse (docs/method.md); it
-  hasn't been measured in Chromium yet. Choices that don't show up as parts
+  development total by 4.6% and made no target worse; measured in
+  Chromium, 59 targets instead of 56 come out shorter than the human code
+  (docs/method.md). Choices that don't show up as parts
   of one color still go one step at a time, and a beam over partial J
   doesn't fix that: on 2024-02-27 the two-circle branch trails for two
   steps before it pulls ahead.
diff --git a/cssynth/codegen.py b/cssynth/codegen.py
index 5e3aa22..c007af2 100644
--- a/cssynth/codegen.py
+++ b/cssynth/codegen.py
@@ -626,8 +626,15 @@ def _elements(shapes, palette, edges, group_of, members):
 def _code(body_opts, elements) -> str:
     if not elements:
         return assemble(body_opts, [])
-    per_element = [min(size_encodings(e), key=lambda d: len(_style(d))) for e in elements]
-    return min((assemble(body_opts, els, tags, inline) for els in (per_element, elements)
+    encodings = [size_encodings(e) for e in elements]
+    # each element's shortest encoding; the same without inset, which can
+    # break a padding the elements would share; the plain properties
+    candidates = []
+    for els in ([min(es, key=lambda d: len(_style(d))) for es in encodings],
+                [min(es[:-1], key=lambda d: len(_style(d))) for es in encodings], elements):
+        if els not in candidates:
+            candidates.append(els)
+    return min((assemble(body_opts, els, tags, inline) for els in candidates
                 for inline in [_inline(els)] for tags in tag_options(els)), key=len)
 
 
diff --git a/docs/method.md b/docs/method.md
index e38ce5b..0cc7d78 100644
--- a/docs/method.md
+++ b/docs/method.md
@@ -176,8 +176,7 @@ The whole compile takes 5–8% more CPU. The two circles on the oval
 circle and its `box-shadow` copy. The cloud goes from 512 to 380, and the
 lotus from 551 to 484 (human: 418) with fewer wrong pixels. Three
 development targets (the oval, the cloud, 2024-03-30) become shorter than
-the human code, counting characters only; the match has yet to be checked
-in Chromium.
+the human code.
 
 Re-searching only the best result can miss a better program: a result that
 starts a little worse sometimes re-searches to a much better one. With
@@ -193,9 +192,23 @@ The whole compile takes 1.5 to 2 times the CPU, but little more wall time
 on 8 cores (the lotus: 16.9 s against 17.8 s). The lotus drops to 291
 characters (human: 418), from the second-best result. The winners come
 from many different variants, so re-searching one fixed extra variant
-wouldn't do the same. It isn't the default yet, since the published numbers
-are the default's, measured in Chromium. (The training figures here include
-the shorter `inset`, which the table above doesn't.)
+wouldn't do the same. It isn't the default yet. (The training figures here
+include the shorter `inset`, which the table above doesn't.)
+
+Measured in Chromium on Linux (the Benchmark workflow), against 0.6.0. J
+here counts Chromium's wrong pixels; these runs predate a fix that keeps a
+shared `padding` when `inset` would be shorter for one element, worth
+about 55 more characters:
+
+| | Total J | Characters | ≥ 99% match | Shorter than human |
+|---|---|---|---|---|
+| 0.6.0 | 52,153 | 23,329 | 66 / 66 | 56 / 66 |
+| Parts, plus the shorter `inset` | 48,985 (−6.1%) | 21,817 | 66 / 66 | 59 / 66 |
+| The same, re-searching the 3 best | 47,179 (−9.5%) | 21,283 | 66 / 66 | 59 / 66 |
+
+The lowest match stays at 99.21%. Re-searching the 3 best puts the lotus
+under the human length and takes the wig over it (227 characters to 285,
+human: 264), for fewer wrong pixels.
 
 ### Length-aware clean-up
 
diff --git a/tests/test_codegen.py b/tests/test_codegen.py
index 3bce7ab..68f8ee8 100644
--- a/tests/test_codegen.py
+++ b/tests/test_codegen.py
@@ -74,3 +74,10 @@ def test_sides_drop_what_css_repeats():
     assert sides(50, 100, 70, 100) == "50px 100px 70px"
     assert sides(50, 100, 50, 0) == "50px 100px 50px 0"
     assert sides(0, 0, 0, 0) == "0"
+
+
+def test_a_shorter_inset_does_not_break_a_shared_padding():
+    pal = np.array([(0, 0, 0), (255, 255, 255)])
+    bowl, dome = Shape(50, 150, 100, 100, 1, radii=(0, 0, 50, 50)), Shape(150, 50, 100, 100, 1, radii=(50, 50, 0, 0))
+    code, _ = emit(pal, 0, [bowl, dome])
+    assert "inset" not in code and code.count("padding:50") == 1

From 683e07fef6ef0f18595e05748df2d10e92c67c26 Mon Sep 17 00:00:00 2001
From: Fedir <98510937+fMindstormer@users.noreply.github.com>
Date: Tue, 10 Nov 2026 10:47:10 -0500
Subject: [PATCH 05/11] Re-search the 3 best results by default

compiler.RESEARCH = 3, used by compile_image, the CLI, bench/run.py, the
ablation and the Benchmark workflow's default input. Measured in Chromium
it lowered total J by 3.7% against re-searching only the best, for 1.5 to
2 times the CPU; with several processes the re-searches run in parallel.
--research 1 (and evaluate.py's top1) keeps the old behavior. The browser
demo passes its checkbox as on/off, so it still re-searches one result.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .github/workflows/benchmark.yml |  2 +-
 README.md                       | 18 ++++++++++--------
 bench/ablation.py               |  2 +-
 bench/evaluate.py               |  8 ++++----
 bench/run.py                    |  7 ++++---
 cssynth/__main__.py             | 10 +++++-----
 cssynth/compiler.py             | 17 ++++++++++++-----
 docs/method.md                  | 22 +++++++++++-----------
 tests/test_compile.py           |  2 +-
 9 files changed, 49 insertions(+), 39 deletions(-)

diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml
index 12cd154..c6a44b9 100644
--- a/.github/workflows/benchmark.yml
+++ b/.github/workflows/benchmark.yml
@@ -7,7 +7,7 @@ on:
     inputs:
       research:
         description: "Re-search the N best results (compile_image research=N)"
-        default: "1"
+        default: "3"
 
 jobs:
   bench:
diff --git a/README.md b/README.md
index 51db12d..b6cb854 100644
--- a/README.md
+++ b/README.md
@@ -124,7 +124,7 @@ cssynth target.png           # prints the code; length and details on stderr
 cssynth -v target.png        # also logs the search
 cssynth --rho 2 target.png   # golf harder (see "Objective")
 cssynth --fast target.png    # skip the re-search: about 2x faster, longer code
-cssynth --research 3 target.png  # re-search the 3 best results: about 2% lower J, more CPU
+cssynth --research 1 target.png  # re-search only the best result: less CPU, longer code
 ```
 
 From Python:
@@ -159,7 +159,7 @@ flowchart LR
     E --> F[simplify for length]
     F --> G[code generation]
     G --> H{lowest J<br>of 16 variants}
-    H --> I[re-search parts<br>of the best]
+    H --> I[re-search parts<br>of the 3 best]
 ```
 
 1. **Palette** (`search.extract_palette`): flat colors with at least 40
@@ -244,14 +244,16 @@ flowchart LR
    but not all: it can pick a cheap shape that leaves more for later.
 8. **Re-search** (`improve.improve`): the greedy search commits to shapes in
    the order they first help, so an early polygon can take a region that a
-   shorter primitive would have drawn. The best result is taken apart and
-   refilled: one polygon at a time (refilled without polygons of its color),
+   shorter primitive would have drawn. Each of the 3 best results (distinct
+   programs, in parallel) is taken apart and refilled: one polygon at a
+   time (refilled without polygons of its color),
    then every shape of one color (refilled as before, and without polygons),
    then every shape of a color whose region falls into parts, one shape per
    part (two circles stacked on an oval). Refills use the winning variant's
-   mode. Each is refined and simplified, and kept when J drops. `--fast`
-   skips this step; `--research 3` runs it on the 3 best results, in
-   parallel, and keeps the best.
+   mode. Each is refined and simplified, and kept when J drops; the best
+   finished program wins, which isn't always the one that started best.
+   `--fast` skips this step; `--research 1` runs it on the best result
+   only.
 
 ## Benchmark
 
@@ -306,7 +308,7 @@ ruff check .
 ```
 cssynth/
   compiler.py   compile_image: run the variants, simplify, keep the lowest J
-  improve.py    re-search: take the best result apart and refill it
+  improve.py    re-search: take the best results apart and refill them
   search.py     palette, base layer, greedy shape search, refine
   shapes.py     the Shape model and a rasterizer that predicts Chromium's
   simplify.py   length-aware clean-up moves
diff --git a/bench/ablation.py b/bench/ablation.py
index 25cdbcb..bdd5fb8 100644
--- a/bench/ablation.py
+++ b/bench/ablation.py
@@ -82,7 +82,7 @@ def work(args):
     name, case = args
     t = time.time()
     code, info, _ = compiler.compile_image(BENCH / "human" / f"{case['id']}.png", variants=variants(name),
-                                           research=name != "no-research")
+                                           research=0 if name == "no-research" else compiler.RESEARCH)
     return {"id": case["id"], "chars": len(code), "wrong_px": info["wrong_px"], "J": info["J"],
             "human_min_len": case["human_min_len"], "seconds": round(time.time() - t, 1)}
 
diff --git a/bench/evaluate.py b/bench/evaluate.py
index 80cf29c..2a387c8 100644
--- a/bench/evaluate.py
+++ b/bench/evaluate.py
@@ -73,9 +73,9 @@ def _no_parts(path):
     return compiler.compile_image(path)
 
 
-def _top3(path):
-    """The default compiler, re-searching the 3 best results."""
-    return compiler.compile_image(path, research=3)
+def _top1(path):
+    """The default compiler, re-searching only the best result (v0.6.0)."""
+    return compiler.compile_image(path, research=1)
 
 
 def _fixes(path):
@@ -107,7 +107,7 @@ def _learned(path):
 
 # name: path -> (code, info, model)
 METHODS = {"full": _full, "fast": _fast, "base": _base, "base-more": _base_more,
-           **{f"full-{n}": _full_more(n) for n in (16, 20)}, "fixes": _fixes, "no-parts": _no_parts, "top3": _top3,
+           **{f"full-{n}": _full_more(n) for n in (16, 20)}, "fixes": _fixes, "no-parts": _no_parts, "top1": _top1,
            "nest": _motifs(["nest"]), "nest-sym": _motifs(["nest", "symmetric"]),
            "all-motifs": _motifs(["nest", "symmetric", "seeded"]),
            "nest-allcolors": _motifs(["nest"], same_color=-1), "learned": _learned}
diff --git a/bench/run.py b/bench/run.py
index 495918a..55b14a8 100755
--- a/bench/run.py
+++ b/bench/run.py
@@ -24,7 +24,7 @@
 
 ROOT = Path(__file__).resolve().parent.parent
 sys.path.insert(0, str(ROOT))
-from cssynth.compiler import compile_image  # noqa: E402
+from cssynth.compiler import RESEARCH, compile_image  # noqa: E402
 from cssynth.shapes import bg_to_json, render_rgb  # noqa: E402
 
 BENCH = ROOT / "bench"
@@ -35,7 +35,7 @@ def match(a, b, tol=8):
     return float((np.abs(a.astype(int) - b.astype(int)).max(2) <= tol).mean() * 100)
 
 
-def work(case, research=1):
+def work(case, research=RESEARCH):
     t = time.time()
     target = BENCH / "human" / f"{case['id']}.png"
     code, info, (pal, bg, T, shapes) = compile_image(target, research=research)
@@ -53,7 +53,8 @@ def main():
     ap = argparse.ArgumentParser()
     ap.add_argument("--only")
     ap.add_argument("--jobs", type=int, default=os.cpu_count())
-    ap.add_argument("--research", type=int, default=1, help="re-search the N best results (see compile_image)")
+    ap.add_argument("--research", type=int, default=RESEARCH,
+                    help="re-search the N best results (see compile_image; default %(default)s)")
     args = ap.parse_args()
     cases = json.loads((BENCH / "cases.json").read_text())
     if args.only:
diff --git a/cssynth/__main__.py b/cssynth/__main__.py
index acde41f..9efd453 100644
--- a/cssynth/__main__.py
+++ b/cssynth/__main__.py
@@ -3,7 +3,7 @@
 import os
 import sys
 
-from .compiler import RHO, compile_image
+from .compiler import RESEARCH, RHO, compile_image
 
 
 def main():
@@ -13,12 +13,12 @@ def main():
     ap.add_argument("--rho", type=float, default=RHO,
                     help="wrong pixels accepted per character saved (default %(default)s)")
     ap.add_argument("-j", "--jobs", type=int, default=os.cpu_count(),
-                    help="processes for the search variants (default: all cores)")
+                    help="processes for the search variants and re-searches (default: all cores)")
     ap.add_argument("--fast", action="store_true",
                     help="skip re-searching parts of the result (several times faster, a little longer code)")
-    ap.add_argument("--research", type=int, default=1, metavar="N",
-                    help="re-search the N best results, not only the best (default %(default)s; 3 lowered the "
-                         "benchmark's J by about 2%% for 1.5 to 2 times the CPU)")
+    ap.add_argument("--research", type=int, default=RESEARCH, metavar="N",
+                    help="re-search the N best results (default %(default)s; 1 takes less CPU, and its J was "
+                         "about 4%% higher on the benchmark)")
     args = ap.parse_args()
     code, info, _ = compile_image(args.image, rho=args.rho, research=0 if args.fast else args.research, jobs=args.jobs,
                                   log=(lambda m: print(m, file=sys.stderr)) if args.v else None)
diff --git a/cssynth/compiler.py b/cssynth/compiler.py
index a002518..bc21df0 100644
--- a/cssynth/compiler.py
+++ b/cssynth/compiler.py
@@ -35,6 +35,12 @@
 # harder (see bench/tradeoff.py).
 RHO = 0.5
 
+# How many of the best distinct results the re-search starts from (see
+# compile_image). On the benchmark, 3 instead of 1 lowered J by 3.7% in
+# Chromium for 1.5 to 2 times the CPU; with several processes they run in
+# parallel, so the wall time grows less.
+RESEARCH = 3
+
 
 def _finish(arr, rho, pal, bg, shapes):
     """(J, code, info) of a stack, scored against the target image."""
@@ -75,12 +81,13 @@ def _research(arr, rho, run, log=None):
     return J2, code, {**info2, "variant": info["variant"], "researched": round(J - J2, 1)}, (pal, bg, T, better)
 
 
-def compile_image(image, log=None, variants=VARIANTS, rho=RHO, research=1, jobs=1):
+def compile_image(image, log=None, variants=VARIANTS, rho=RHO, research=RESEARCH, jobs=1):
     """image: a path or a PIL image. research: how many of the best distinct
-    results to re-search (see improve; 0 or False: none); the best one after
-    re-searching wins. A result that isn't the best can re-search to a better
-    one, so more costs CPU and can only lower J. jobs: processes to run the
-    variants and re-searches in (the result is the same for any number)."""
+    results to re-search (see improve; 0 or False: none, True: 1); the best
+    one after re-searching wins. A result that isn't the best can re-search
+    to a better one, so more costs CPU and can only lower J. jobs: processes
+    to run the variants and re-searches in (the result is the same for any
+    number)."""
     img = (image if isinstance(image, Image.Image) else Image.open(image)).convert("RGB")
     if img.size != (400, 300):
         img = img.resize((400, 300), Image.BOX)
diff --git a/docs/method.md b/docs/method.md
index 0cc7d78..5d74dde 100644
--- a/docs/method.md
+++ b/docs/method.md
@@ -141,12 +141,12 @@ fewer shapes.
 
 The greedy search commits to shapes in the order they first help. An early
 polygon can take a region that a shorter primitive would have drawn, and
-everything around it is then fitted to that choice. After the variants, the
-best result goes through a small large-neighborhood search: remove one
-polygon (and forbid polygons of its color), or every shape of one color
-(with or without polygons), refill the gap with the greedy search from the
-remaining stack (in the winning variant's mode), refine, simplify, and
-keep the result if J drops.
+everything around it is then fitted to that choice. After the variants,
+each of the three best results goes through a small large-neighborhood
+search: remove one polygon (and forbid polygons of its color), or every
+shape of one color (with or without polygons), refill the gap with the
+greedy search from the remaining stack (in that result's variant mode),
+refine, simplify, and keep the result if J drops.
 
 A third neighborhood redraws a color whose region falls into parts. The
 region is thinned by its distance to the edge until it comes apart; each
@@ -179,9 +179,9 @@ development targets (the oval, the cloud, 2024-03-30) become shorter than
 the human code.
 
 Re-searching only the best result can miss a better program: a result that
-starts a little worse sometimes re-searches to a much better one. With
-`research=3`, the three best distinct results are re-searched (in parallel
-when there are several processes) and the best wins:
+starts a little worse sometimes re-searches to a much better one. So the
+three best distinct results are re-searched (in parallel when there are
+several processes) and the best wins (`research=3`, the default):
 
 | Split | Re-search the best | The 2 best | The 3 best | Re-search CPU (3 best) |
 |---|---|---|---|---|
@@ -192,8 +192,8 @@ The whole compile takes 1.5 to 2 times the CPU, but little more wall time
 on 8 cores (the lotus: 16.9 s against 17.8 s). The lotus drops to 291
 characters (human: 418), from the second-best result. The winners come
 from many different variants, so re-searching one fixed extra variant
-wouldn't do the same. It isn't the default yet. (The training figures here
-include the shorter `inset`, which the table above doesn't.)
+wouldn't do the same. (The training figures here include the shorter
+`inset`, which the table above doesn't.)
 
 Measured in Chromium on Linux (the Benchmark workflow), against 0.6.0. J
 here counts Chromium's wrong pixels; these runs predate a fix that keeps a
diff --git a/tests/test_compile.py b/tests/test_compile.py
index f54ec04..1308a3f 100644
--- a/tests/test_compile.py
+++ b/tests/test_compile.py
@@ -70,5 +70,5 @@ def test_parallel_variants_give_the_same_code(tmp_path):
 def test_researching_more_results_never_scores_worse(tmp_path):
     path = disc_and_triangle(tmp_path)
     code, info, _ = compile_image(path, research=3, jobs=3)
-    assert info["J"] <= compile_image(path)[1]["J"]
+    assert info["J"] <= compile_image(path, research=1)[1]["J"]
     assert compile_image(path, research=3)[0] == code

From bd0fcf6546543a94b6c77cd3e654f8892d5c4514 Mon Sep 17 00:00:00 2001
From: Fedir <98510937+fMindstormer@users.noreply.github.com>
Date: Tue, 10 Nov 2026 11:09:43 -0500
Subject: [PATCH 06/11] 0.7.0: version, ablation rows and a held-out workflow

- pyproject: 0.7.0.
- Ablation: no-parts and research-1 (re-search only the best) rows.
- evaluate.py: v0.6 and v0.6-N, the search of 0.6.0 with N starts per
  step, for the held-out comparison at equal compute.
- A Held-out workflow: generates one set, compiles it with each method
  one after another on one runner, renders in Chromium and prints the
  summary.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .github/workflows/ablation.yml |  2 +-
 .github/workflows/heldout.yml  | 46 ++++++++++++++++++++++++++++++++++
 .gitignore                     |  1 +
 bench/ablation.py              |  6 +++--
 bench/build_page.py            |  3 ++-
 bench/evaluate.py              | 14 +++++++++--
 pyproject.toml                 |  2 +-
 7 files changed, 67 insertions(+), 7 deletions(-)
 create mode 100644 .github/workflows/heldout.yml

diff --git a/.github/workflows/ablation.yml b/.github/workflows/ablation.yml
index b98a34e..9b0bb67 100644
--- a/.github/workflows/ablation.yml
+++ b/.github/workflows/ablation.yml
@@ -33,7 +33,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        config: [full, no-research, no-cleanup, no-exact-grouping, no-box-shadows, no-tag-rules,
+        config: [full, no-research, no-parts, research-1, no-cleanup, no-exact-grouping, no-box-shadows, no-tag-rules,
                  no-clip-reuse, top-only, no-corner-split, no-outline-polygons, no-length-aware, no-gradient-base,
                  no-soft-stops, no-motifs]
     steps:
diff --git a/.github/workflows/heldout.yml b/.github/workflows/heldout.yml
new file mode 100644
index 0000000..9874128
--- /dev/null
+++ b/.github/workflows/heldout.yml
@@ -0,0 +1,46 @@
+# Held-out evaluation: generate one held-out set of structural families
+# (bench/families.py), compile it with each method one after another on the
+# same runner (so CPU times compare), render in Chromium and print the
+# summary (bench/evaluate.py). Run it by hand from the Actions tab, once per
+# version: a set is spent once it has been run (see docs/method.md).
+name: Held-out
+
+on:
+  workflow_dispatch:
+    inputs:
+      set:
+        description: "Held-out set, a key of HELDOUT_SEEDS in bench/families.py"
+        required: true
+      methods:
+        description: "Methods of bench/evaluate.py, space-separated"
+        required: true
+        default: "full"
+
+jobs:
+  evaluate:
+    runs-on: ubuntu-latest
+    timeout-minutes: 330
+    env:
+      SET: ${{ inputs.set }}
+      METHODS: ${{ inputs.methods }}
+    steps:
+      - uses: actions/checkout@v7
+      - uses: actions/setup-python@v7
+        with:
+          python-version: "3.13"
+      - uses: actions/setup-node@v7
+        with:
+          node-version: 22
+      - run: pip install -e .
+      - run: npm ci
+        working-directory: bench
+      - run: python bench/families.py "$SET"
+      - run: for m in $METHODS; do python bench/evaluate.py --set "$SET" --method "$m"; done
+      - run: python bench/evaluate.py --set "$SET" --compare $METHODS | tee bench/eval/summary.txt
+      - uses: actions/upload-artifact@v7
+        with:
+          name: heldout
+          path: |
+            bench/${{ inputs.set }}/cases.json
+            bench/${{ inputs.set }}/*.html
+            bench/eval/
diff --git a/.gitignore b/.gitignore
index 798d80f..511dfc9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -24,6 +24,7 @@ bench/train/
 bench/learn/
 bench/heldout2/*.png
 bench/heldout3/*.png
+bench/heldout4/*.png
 
 # the demo site, assembled locally (demo/README.md)
 demo/site/
diff --git a/bench/ablation.py b/bench/ablation.py
index bdd5fb8..c48e6cc 100644
--- a/bench/ablation.py
+++ b/bench/ablation.py
@@ -40,7 +40,9 @@ def _no_simplify(T, bg, pal, shapes, **kw):
 # name: (what is switched off, how)
 ABLATIONS = {
     "full": ("nothing", lambda: None),
-    "no-research": ("re-search of the best result", None),
+    "no-research": ("re-search of the best results", None),
+    "no-parts": ("redrawing a color in parts (re-search)", lambda: setattr(improve, "PARTS", False)),
+    "research-1": ("re-searching the 3 best results (the best only)", None),
     "no-cleanup": ("length-aware clean-up", lambda: (setattr(compiler, "simplify", _no_simplify),
                                                      setattr(improve, "simplify", _no_simplify))),
     "no-exact-grouping": ("exact (CP-SAT) shadow grouping; greedy only",
@@ -82,7 +84,7 @@ def work(args):
     name, case = args
     t = time.time()
     code, info, _ = compiler.compile_image(BENCH / "human" / f"{case['id']}.png", variants=variants(name),
-                                           research=0 if name == "no-research" else compiler.RESEARCH)
+                                           research={"no-research": 0, "research-1": 1}.get(name, compiler.RESEARCH))
     return {"id": case["id"], "chars": len(code), "wrong_px": info["wrong_px"], "J": info["J"],
             "human_min_len": case["human_min_len"], "seconds": round(time.time() - t, 1)}
 
diff --git a/bench/build_page.py b/bench/build_page.py
index fb0b2ae..3d9f59c 100755
--- a/bench/build_page.py
+++ b/bench/build_page.py
@@ -19,7 +19,8 @@ def fmt(n):
 
 
 ABLATION_NAMES = {
-    "no-research": "Re-search of the best result", "no-cleanup": "Length-aware clean-up",
+    "no-research": "Re-search of the best results", "no-parts": "Redrawing a color in parts (re-search)",
+    "research-1": "Re-searching the 3 best results (the best only)", "no-cleanup": "Length-aware clean-up",
     "no-exact-grouping": "Exact shadow grouping (greedy kept)", "no-box-shadows": "Box-shadows",
     "no-tag-rules": "Per-tag rules (&lt;i&gt;, &lt;u&gt;, &lt;s&gt;)",
     "no-clip-reuse": "clip-path reuse for copies and mirrors",
diff --git a/bench/evaluate.py b/bench/evaluate.py
index 2a387c8..42b0c9f 100644
--- a/bench/evaluate.py
+++ b/bench/evaluate.py
@@ -68,16 +68,25 @@ def run(path):
 
 
 def _no_parts(path):
-    """The default compiler without re-searching colors in parts (v0.6.0)."""
+    """The default compiler without redrawing colors in parts."""
     improve.PARTS = False
     return compiler.compile_image(path)
 
 
 def _top1(path):
-    """The default compiler, re-searching only the best result (v0.6.0)."""
+    """The default compiler, re-searching only the best result."""
     return compiler.compile_image(path, research=1)
 
 
+def _v06(starts=10):
+    """The search of v0.6.0: no parts, the best result re-searched only;
+    climbing more starts per step, for comparisons at equal compute."""
+    def run(path):
+        improve.PARTS, search.TOP_STARTS = False, starts
+        return compiler.compile_image(path, research=1)
+    return run
+
+
 def _fixes(path):
     """Everything but motifs."""
     search.MOTIFS = set()
@@ -108,6 +117,7 @@ def _learned(path):
 # name: path -> (code, info, model)
 METHODS = {"full": _full, "fast": _fast, "base": _base, "base-more": _base_more,
            **{f"full-{n}": _full_more(n) for n in (16, 20)}, "fixes": _fixes, "no-parts": _no_parts, "top1": _top1,
+           "v0.6": _v06(), **{f"v0.6-{n}": _v06(n) for n in (16, 20, 24)},
            "nest": _motifs(["nest"]), "nest-sym": _motifs(["nest", "symmetric"]),
            "all-motifs": _motifs(["nest", "symmetric", "seeded"]),
            "nest-allcolors": _motifs(["nest"], same_color=-1), "learned": _learned}
diff --git a/pyproject.toml b/pyproject.toml
index 9f13c5d..7ab30c0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "cssynth"
-version = "0.6.0"
+version = "0.7.0"
 description = "Compile flat images into short HTML/CSS, for CSSBattle-style code golf"
 readme = "docs/pypi.md"    # the GitHub README uses relative links and a diagram PyPI can't show
 license = "MIT"

From 23a51e76bdddd894c29023164dde5d5067bc1920 Mon Sep 17 00:00:00 2001
From: Fedir <98510937+fMindstormer@users.noreply.github.com>
Date: Tue, 10 Nov 2026 11:34:17 -0500
Subject: [PATCH 07/11] evaluate.py: v0.6-48, 0.6.0's search at 0.7.0's CPU

On every third training target, 0.7.0 took 1,456 s of CPU against 503 s
for 0.6.0's search; each extra start per step costs about 33 s there, so
48 starts spend at least as much (20 and 24 starts: 823 s and 964 s).
More starts didn't lower J (22,640 at 10, 22,655 at 20, 22,673 at 24).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 bench/evaluate.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/bench/evaluate.py b/bench/evaluate.py
index 42b0c9f..d70a29a 100644
--- a/bench/evaluate.py
+++ b/bench/evaluate.py
@@ -117,7 +117,7 @@ def _learned(path):
 # name: path -> (code, info, model)
 METHODS = {"full": _full, "fast": _fast, "base": _base, "base-more": _base_more,
            **{f"full-{n}": _full_more(n) for n in (16, 20)}, "fixes": _fixes, "no-parts": _no_parts, "top1": _top1,
-           "v0.6": _v06(), **{f"v0.6-{n}": _v06(n) for n in (16, 20, 24)},
+           "v0.6": _v06(), **{f"v0.6-{n}": _v06(n) for n in (16, 20, 24, 48)},
            "nest": _motifs(["nest"]), "nest-sym": _motifs(["nest", "symmetric"]),
            "all-motifs": _motifs(["nest", "symmetric", "seeded"]),
            "nest-allcolors": _motifs(["nest"], same_color=-1), "learned": _learned}

From 4f781fd233191b244456454f131c7f116cc36f8c Mon Sep 17 00:00:00 2001
From: Fedir <98510937+fMindstormer@users.noreply.github.com>
Date: Tue, 10 Nov 2026 12:27:03 -0500
Subject: [PATCH 08/11] Linux results and ablation for 0.7.0

On the 66 development targets, rendered in Chromium: 60 shorter than the
human code (was 56), 21,175 characters (was 23,329), all 66 at >= 99%
match (lowest 99.21%), mean match 99.84%. Ablation: the re-search is now
worth 14.4% of J, redrawing a color in parts 6.1%, re-searching the 3
best rather than the best 2.4%.

The blog's "where it still loses" example (two circles on an oval) is now
solved; it moves to a section on comparing whole programs, and the pin
(2024-03-26, no gradients on shapes) takes its place. Captions follow the
new programs for the sunburst and the rings.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 README.md                           |   47 +-
 bench/ablation.json                 |  132 +-
 bench/cssynth-results.html          |   20 +-
 bench/out/ks-#159-portal.html       |    2 +-
 bench/out/ks-#160-donut.html        |    2 +-
 bench/out/ks-03-pushbutton.html     |    2 +-
 bench/out/ks-05-acidrains.html      |    2 +-
 bench/out/ks-05-acidrains.png       |  Bin 5766 -> 5773 bytes
 bench/out/ks-07-leafytrial.html     |    2 +-
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 bench/out/ks-136-alieneye.html      |    2 +-
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 bench/out/ks-139-lamb.html          |    2 +-
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 bench/out/ks-37-tunnel.html         |    2 +-
 bench/out/ks-46-mountains.html      |    2 +-
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 bench/out/ks-85-pythagoras.html     |    2 +-
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 80 files changed, 2108 insertions(+), 2663 deletions(-)

diff --git a/README.md b/README.md
index b6cb854..962adc2 100644
--- a/README.md
+++ b/README.md
@@ -40,10 +40,10 @@ Chromium and compared pixel by pixel:
 
 | | |
 |---|---|
-| Shorter than the human solution (minified) | **56 of 66** |
-| Total code length | **23,329** characters (human: 35,532) |
+| Shorter than the human solution (minified) | **60 of 66** |
+| Total code length | **21,175** characters (human: 35,532) |
 | Targets at ≥ 99% pixel match | **66 of 66** (lowest 99.21%) |
-| Mean pixel match | 99.82% |
+| Mean pixel match | 99.84% |
 
 The human solutions are readable code from two public repositories, minified
 but not golfed. Top CSSBattle players write far shorter code, so these
@@ -61,25 +61,28 @@ rows don't add up.
 
 | Switched off | Total J | Characters | Shorter than human |
 |---|---|---|---|
-| nothing (full compiler) | 60,441 | 23,329 | 56 / 66 |
-| Insertion at any depth (shapes go on top) | 64,637 (+6.9%) | 24,887 (+1,558) | 54 / 66 |
-| Polygons traced from outlines | 64,449 (+6.6%) | 25,791 (+2,462) | 52 / 66 |
-| Re-search of the best result | 64,444 (+6.6%) | 24,652 (+1,323) | 55 / 66 |
-| Gradient backgrounds | 63,265 (+4.7%) | 24,077 (+748) | 54 / 66 |
-| Length-aware shape choice (by pixels only) | 62,921 (+4.1%) | 24,017 (+688) | 54 / 66 |
-| Length-aware clean-up | 61,982 (+2.5%) | 24,914 (+1,585) | 52 / 66 |
-| Splitting regions that touch at corners | 61,705 (+2.1%) | 24,005 (+676) | 55 / 66 |
-| Nested-ring motifs | 61,309 (+1.4%) | 23,495 (+166) | 56 / 66 |
-| Box-shadows | 61,258 (+1.4%) | 24,150 (+821) | 56 / 66 |
-| Soft gradient stops | 61,106 (+1.1%) | 23,680 (+351) | 55 / 66 |
-| Per-tag rules (`<i>`, `<u>`, `<s>`) | 60,807 (+0.6%) | 23,583 (+254) | 55 / 66 |
-| `clip-path` reuse for copies and mirrors | 60,708 (+0.4%) | 23,544 (+215) | 55 / 66 |
-| Exact (CP-SAT) shadow grouping, greedy kept | 60,554 (+0.2%) | 23,442 (+113) | 56 / 66 |
-
-Inserting shapes below existing ones, outline polygons and the re-search
-matter most; gradient backgrounds and choosing shapes by length as well as
-pixels come next. Exact shadow grouping and `clip-path` reuse are small on
-average but decide a few targets.
+| nothing (full compiler) | 55,925 | 21,175 | 60 / 66 |
+| Re-search of the best results | 63,980 (+14.4%) | 24,086 (+2,911) | 56 / 66 |
+| Redrawing a color in parts (re-search) | 59,350 (+6.1%) | 22,692 (+1,517) | 57 / 66 |
+| Polygons traced from outlines | 59,032 (+5.6%) | 23,074 (+1,899) | 59 / 66 |
+| Insertion at any depth (shapes go on top) | 58,545 (+4.7%) | 21,819 (+644) | 59 / 66 |
+| Length-aware shape choice (by pixels only) | 57,824 (+3.4%) | 21,386 (+211) | 61 / 66 |
+| Gradient backgrounds | 57,462 (+2.7%) | 21,514 (+339) | 59 / 66 |
+| Re-searching the 3 best results (only the best) | 57,269 (+2.4%) | 21,691 (+516) | 60 / 66 |
+| Nested-ring motifs | 56,758 (+1.5%) | 21,260 (+85) | 60 / 66 |
+| Box-shadows | 56,674 (+1.3%) | 21,876 (+701) | 60 / 66 |
+| Length-aware clean-up | 56,567 (+1.1%) | 21,779 (+604) | 61 / 66 |
+| `clip-path` reuse for copies and mirrors | 56,184 (+0.5%) | 21,254 (+79) | 59 / 66 |
+| Per-tag rules (`<i>`, `<u>`, `<s>`) | 56,171 (+0.4%) | 21,293 (+118) | 59 / 66 |
+| Splitting regions that touch at corners | 55,990 (+0.1%) | 21,158 (−17) | 61 / 66 |
+| Exact (CP-SAT) shadow grouping, greedy kept | 55,971 (+0.1%) | 21,221 (+46) | 60 / 66 |
+| Soft gradient stops | 55,961 (+0.1%) | 21,167 (−8) | 60 / 66 |
+
+The re-search matters most, and its step that redraws a color in parts is
+worth 6.1% on its own; outline polygons and inserting shapes below existing
+ones come next. Three parts now move the total by 0.1% or less: splitting
+regions at corners, soft gradient stops and exact shadow grouping. Rows that
+show more targets shorter than human (61) do so at a higher total J.
 
 ### Beyond the development set
 
diff --git a/bench/ablation.json b/bench/ablation.json
index e707869..79f5f67 100644
--- a/bench/ablation.json
+++ b/bench/ablation.json
@@ -2,127 +2,145 @@
  {
   "config": "full",
   "off": "nothing",
-  "chars": 23329,
-  "J": 60441,
-  "shorter": 56,
-  "cpu_min": 84.6,
+  "chars": 21175,
+  "J": 55925,
+  "shorter": 60,
+  "cpu_min": 251.1,
   "n": 66
  },
  {
   "config": "no-research",
-  "off": "re-search of the best result",
-  "chars": 24652,
-  "J": 64444,
-  "shorter": 55,
-  "cpu_min": 88.3,
+  "off": "re-search of the best results",
+  "chars": 24086,
+  "J": 63980,
+  "shorter": 56,
+  "cpu_min": 94.3,
+  "n": 66
+ },
+ {
+  "config": "no-parts",
+  "off": "redrawing a color in parts (re-search)",
+  "chars": 22692,
+  "J": 59350,
+  "shorter": 57,
+  "cpu_min": 218.1,
+  "n": 66
+ },
+ {
+  "config": "research-1",
+  "off": "re-searching the 3 best results (the best only)",
+  "chars": 21691,
+  "J": 57269,
+  "shorter": 60,
+  "cpu_min": 87.4,
   "n": 66
  },
  {
   "config": "no-cleanup",
   "off": "length-aware clean-up",
-  "chars": 24914,
-  "J": 61982,
-  "shorter": 52,
-  "cpu_min": 69.5,
+  "chars": 21779,
+  "J": 56567,
+  "shorter": 61,
+  "cpu_min": 136.5,
   "n": 66
  },
  {
   "config": "no-exact-grouping",
   "off": "exact (CP-SAT) shadow grouping; greedy only",
-  "chars": 23442,
-  "J": 60554,
-  "shorter": 56,
-  "cpu_min": 127.8,
+  "chars": 21221,
+  "J": 55971,
+  "shorter": 60,
+  "cpu_min": 114.6,
   "n": 66
  },
  {
   "config": "no-box-shadows",
   "off": "box-shadows",
-  "chars": 24150,
-  "J": 61258,
-  "shorter": 56,
-  "cpu_min": 58.8,
+  "chars": 21876,
+  "J": 56674,
+  "shorter": 60,
+  "cpu_min": 259.3,
   "n": 66
  },
  {
   "config": "no-tag-rules",
   "off": "per-tag rules (<i>, <u>, <s>)",
-  "chars": 23583,
-  "J": 60807,
-  "shorter": 55,
-  "cpu_min": 122.0,
+  "chars": 21293,
+  "J": 56171,
+  "shorter": 59,
+  "cpu_min": 239.5,
   "n": 66
  },
  {
   "config": "no-clip-reuse",
   "off": "clip-path reuse for copies and mirrors",
-  "chars": 23544,
-  "J": 60708,
-  "shorter": 55,
-  "cpu_min": 130.6,
+  "chars": 21254,
+  "J": 56184,
+  "shorter": 59,
+  "cpu_min": 226.7,
   "n": 66
  },
  {
   "config": "top-only",
   "off": "insertion at any depth; new shapes go on top",
-  "chars": 24887,
-  "J": 64637,
-  "shorter": 54,
-  "cpu_min": 63.7,
+  "chars": 21819,
+  "J": 58545,
+  "shorter": 59,
+  "cpu_min": 138.0,
   "n": 66
  },
  {
   "config": "no-corner-split",
   "off": "splitting regions that touch at corners",
-  "chars": 24005,
-  "J": 61705,
-  "shorter": 55,
-  "cpu_min": 131.3,
+  "chars": 21158,
+  "J": 55990,
+  "shorter": 61,
+  "cpu_min": 168.6,
   "n": 66
  },
  {
   "config": "no-outline-polygons",
   "off": "polygons traced from outlines",
-  "chars": 25791,
-  "J": 64449,
-  "shorter": 52,
-  "cpu_min": 39.9,
+  "chars": 23074,
+  "J": 59032,
+  "shorter": 59,
+  "cpu_min": 134.4,
   "n": 66
  },
  {
   "config": "no-length-aware",
   "off": "length-aware shape choice; by pixels only",
-  "chars": 24017,
-  "J": 62921,
-  "shorter": 54,
-  "cpu_min": 88.8,
+  "chars": 21386,
+  "J": 57824,
+  "shorter": 61,
+  "cpu_min": 127.5,
   "n": 66
  },
  {
   "config": "no-gradient-base",
   "off": "gradient backgrounds",
-  "chars": 24077,
-  "J": 63265,
-  "shorter": 54,
-  "cpu_min": 95.5,
+  "chars": 21514,
+  "J": 57462,
+  "shorter": 59,
+  "cpu_min": 126.4,
   "n": 66
  },
  {
   "config": "no-soft-stops",
   "off": "soft gradient stops",
-  "chars": 23680,
-  "J": 61106,
-  "shorter": 55,
-  "cpu_min": 110.3,
+  "chars": 21167,
+  "J": 55961,
+  "shorter": 60,
+  "cpu_min": 227.8,
   "n": 66
  },
  {
   "config": "no-motifs",
   "off": "nested-ring motifs",
-  "chars": 23495,
-  "J": 61309,
-  "shorter": 56,
-  "cpu_min": 120.7,
+  "chars": 21260,
+  "J": 56758,
+  "shorter": 60,
+  "cpu_min": 155.5,
   "n": 66
  }
 ]
\ No newline at end of file
diff --git a/bench/cssynth-results.html b/bench/cssynth-results.html
index a56d4bf..a05a9d5 100644
--- a/bench/cssynth-results.html
+++ b/bench/cssynth-results.html
@@ -118,15 +118,15 @@ <h1>cssynth</h1>
   <section aria-label="Summary">
     <div class="stats">
       <div><b>66/66</b><span>targets at 99% match or better (66 at 98%+)</span></div>
-      <div><b>327</b><span>median characters on those</span></div>
+      <div><b>302</b><span>median characters on those</span></div>
       <div><b>480</b><span>median for the human solutions, minified</span></div>
-      <div><b>56</b><span>of those shorter than the human code</span></div>
+      <div><b>60</b><span>of those shorter than the human code</span></div>
     </div>
     <div class="stats" style="margin-top:1rem">
       <div><b>64 &rarr; 66</b><span>targets at 99%+ match, first version &rarr; now</span></div>
-      <div><b>10 &rarr; 22</b><span>targets at a 100% match</span></div>
-      <div><b>34,963 &rarr; 23,329</b><span>characters across all targets</span></div>
-      <div><b>&minus;33%</b><span>total code length since the first version</span></div>
+      <div><b>10 &rarr; 24</b><span>targets at a 100% match</span></div>
+      <div><b>34,963 &rarr; 21,175</b><span>characters across all targets</span></div>
+      <div><b>&minus;39%</b><span>total code length since the first version</span></div>
     </div>
     <p class="note">Match counts pixels whose color is within 8/255 per channel of the target. The human solutions come from two public GitHub repos and are written to be readable, not golfed. Top CSSBattle players are far shorter, so these lengths are a baseline, not a leaderboard. All results, including the trade-off table, are rendered on Linux with the same Chromium build as the targets.</p>
   </section>
@@ -148,11 +148,11 @@ <h3>Outline polygons</h3>
       </article>
       <article class="change">
         <h3>Gradient backgrounds</h3>
-        <p>For striped pictures the compiler fits a hard-stop <code>linear-gradient</code> at any angle: every pixel is projected onto the gradient line, and dynamic programming picks the band boundaries that minimize wrong pixels plus a cost per stop. Shapes are then layered on top as before. 9 targets use one.</p>
+        <p>For striped pictures the compiler fits a hard-stop <code>linear-gradient</code> at any angle: every pixel is projected onto the gradient line, and dynamic programming picks the band boundaries that minimize wrong pixels plus a cost per stop. Shapes are then layered on top as before. 5 targets use one.</p>
       </article>
       <article class="change">
         <h3>Exactly optimal shadow grouping</h3>
-        <p>Choosing which shapes become <code>box-shadow</code>s of which, together with a valid paint order, is now solved exactly with a constraint solver: each shape gets a paint position, shadows share their anchor's, and every pair that must stay in order needs strictly increasing positions unless they share a group. Of 30 targets with any grouping, it beat the greedy grouping on 7 (42 characters) and proved optimality on 28.</p>
+        <p>Choosing which shapes become <code>box-shadow</code>s of which, together with a valid paint order, is now solved exactly with a constraint solver: each shape gets a paint position, shadows share their anchor's, and every pair that must stay in order needs strictly increasing positions unless they share a group. Of 31 targets with any grouping, it beat the greedy grouping on 7 (51 characters) and proved optimality on 30.</p>
       </article>
       <article class="change">
         <h3>Scoring by code length</h3>
@@ -189,13 +189,13 @@ <h2 id="h-all">All 66 targets</h2>
   <section aria-labelledby="h-abl">
     <h2 id="h-abl">What each part buys</h2>
     <p>Each row recompiles all 66 targets with one technique switched off. Scores come from the compiler's own rasterizer rather than Chromium, and the parts overlap, so the rows don't add up. Inserting shapes below existing ones and re-searching the result matter most.</p>
-    <div class="scroll"><table class="tbl"><thead><tr><th>Switched off</th><th>Total J</th><th>Characters</th><th>Shorter than human</th></tr></thead><tbody><tr><td>nothing (full compiler)</td><td>60,441</td><td>23,329</td><td>56 / 66</td></tr><tr><td>Insertion at any depth</td><td>64,637 <span class=muted>(+6.9%)</span></td><td>24,887 <span class=muted>(+1,558)</span></td><td>54 / 66</td></tr><tr><td>Polygons traced from outlines</td><td>64,449 <span class=muted>(+6.6%)</span></td><td>25,791 <span class=muted>(+2,462)</span></td><td>52 / 66</td></tr><tr><td>Re-search of the best result</td><td>64,444 <span class=muted>(+6.6%)</span></td><td>24,652 <span class=muted>(+1,323)</span></td><td>55 / 66</td></tr><tr><td>Gradient backgrounds</td><td>63,265 <span class=muted>(+4.7%)</span></td><td>24,077 <span class=muted>(+748)</span></td><td>54 / 66</td></tr><tr><td>Length-aware shape choice</td><td>62,921 <span class=muted>(+4.1%)</span></td><td>24,017 <span class=muted>(+688)</span></td><td>54 / 66</td></tr><tr><td>Length-aware clean-up</td><td>61,982 <span class=muted>(+2.5%)</span></td><td>24,914 <span class=muted>(+1,585)</span></td><td>52 / 66</td></tr><tr><td>Splitting regions that touch at corners</td><td>61,705 <span class=muted>(+2.1%)</span></td><td>24,005 <span class=muted>(+676)</span></td><td>55 / 66</td></tr><tr><td>Nested-ring motifs</td><td>61,309 <span class=muted>(+1.4%)</span></td><td>23,495 <span class=muted>(+166)</span></td><td>56 / 66</td></tr><tr><td>Box-shadows</td><td>61,258 <span class=muted>(+1.4%)</span></td><td>24,150 <span class=muted>(+821)</span></td><td>56 / 66</td></tr><tr><td>Soft gradient stops</td><td>61,106 <span class=muted>(+1.1%)</span></td><td>23,680 <span class=muted>(+351)</span></td><td>55 / 66</td></tr><tr><td>Per-tag rules (&lt;i&gt;, &lt;u&gt;, &lt;s&gt;)</td><td>60,807 <span class=muted>(+0.6%)</span></td><td>23,583 <span class=muted>(+254)</span></td><td>55 / 66</td></tr><tr><td>clip-path reuse for copies and mirrors</td><td>60,708 <span class=muted>(+0.4%)</span></td><td>23,544 <span class=muted>(+215)</span></td><td>55 / 66</td></tr><tr><td>Exact shadow grouping (greedy kept)</td><td>60,554 <span class=muted>(+0.2%)</span></td><td>23,442 <span class=muted>(+113)</span></td><td>56 / 66</td></tr></tbody></table></div>
+    <div class="scroll"><table class="tbl"><thead><tr><th>Switched off</th><th>Total J</th><th>Characters</th><th>Shorter than human</th></tr></thead><tbody><tr><td>nothing (full compiler)</td><td>55,925</td><td>21,175</td><td>60 / 66</td></tr><tr><td>Re-search of the best results</td><td>63,980 <span class=muted>(+14.4%)</span></td><td>24,086 <span class=muted>(+2,911)</span></td><td>56 / 66</td></tr><tr><td>Redrawing a color in parts (re-search)</td><td>59,350 <span class=muted>(+6.1%)</span></td><td>22,692 <span class=muted>(+1,517)</span></td><td>57 / 66</td></tr><tr><td>Polygons traced from outlines</td><td>59,032 <span class=muted>(+5.6%)</span></td><td>23,074 <span class=muted>(+1,899)</span></td><td>59 / 66</td></tr><tr><td>Insertion at any depth</td><td>58,545 <span class=muted>(+4.7%)</span></td><td>21,819 <span class=muted>(+644)</span></td><td>59 / 66</td></tr><tr><td>Length-aware shape choice</td><td>57,824 <span class=muted>(+3.4%)</span></td><td>21,386 <span class=muted>(+211)</span></td><td>61 / 66</td></tr><tr><td>Gradient backgrounds</td><td>57,462 <span class=muted>(+2.7%)</span></td><td>21,514 <span class=muted>(+339)</span></td><td>59 / 66</td></tr><tr><td>Re-searching the 3 best results (the best only)</td><td>57,269 <span class=muted>(+2.4%)</span></td><td>21,691 <span class=muted>(+516)</span></td><td>60 / 66</td></tr><tr><td>Nested-ring motifs</td><td>56,758 <span class=muted>(+1.5%)</span></td><td>21,260 <span class=muted>(+85)</span></td><td>60 / 66</td></tr><tr><td>Box-shadows</td><td>56,674 <span class=muted>(+1.3%)</span></td><td>21,876 <span class=muted>(+701)</span></td><td>60 / 66</td></tr><tr><td>Length-aware clean-up</td><td>56,567 <span class=muted>(+1.1%)</span></td><td>21,779 <span class=muted>(+604)</span></td><td>61 / 66</td></tr><tr><td>clip-path reuse for copies and mirrors</td><td>56,184 <span class=muted>(+0.5%)</span></td><td>21,254 <span class=muted>(+79)</span></td><td>59 / 66</td></tr><tr><td>Per-tag rules (&lt;i&gt;, &lt;u&gt;, &lt;s&gt;)</td><td>56,171 <span class=muted>(+0.4%)</span></td><td>21,293 <span class=muted>(+118)</span></td><td>59 / 66</td></tr><tr><td>Splitting regions that touch at corners</td><td>55,990 <span class=muted>(+0.1%)</span></td><td>21,158 <span class=muted>(−17)</span></td><td>61 / 66</td></tr><tr><td>Exact shadow grouping (greedy kept)</td><td>55,971 <span class=muted>(+0.1%)</span></td><td>21,221 <span class=muted>(+46)</span></td><td>60 / 66</td></tr><tr><td>Soft gradient stops</td><td>55,961 <span class=muted>(+0.1%)</span></td><td>21,167 <span class=muted>(−8)</span></td><td>60 / 66</td></tr></tbody></table></div>
   </section>
 
   <section aria-labelledby="h-trade">
     <h2 id="h-trade">Trading match for length</h2>
     <p>&rho; is how many wrong pixels the compiler accepts to save one character. CSSBattle tells players to reach a 100% match before cutting characters, so the default is cautious. Raising &rho; golfs harder. Each row below is the whole benchmark re-run at that setting, rendered in Chromium and scored.</p>
-    <div class="scroll"><table class="tbl"><thead><tr><th>Setting</th><th>Targets at 99%+</th><th>At 99.5%+</th><th>Total characters</th><th>Median match</th></tr></thead><tbody><tr><td>default (&rho; = 0.5)</td><td>66</td><td>59</td><td>23,329</td><td>99.90%</td></tr><tr><td>&rho; = 0.25</td><td>66</td><td>59</td><td>23,329</td><td>99.90%</td></tr><tr><td>&rho; = 1</td><td>66</td><td>59</td><td>22,562 <span class=muted>(−3%)</span></td><td>99.90%</td></tr><tr><td>&rho; = 2</td><td>66</td><td>59</td><td>22,013 <span class=muted>(−6%)</span></td><td>99.89%</td></tr><tr><td>&rho; = 4</td><td>66</td><td>59</td><td>21,452 <span class=muted>(−8%)</span></td><td>99.83%</td></tr></tbody></table></div>
+    <div class="scroll"><table class="tbl"><thead><tr><th>Setting</th><th>Targets at 99%+</th><th>At 99.5%+</th><th>Total characters</th><th>Median match</th></tr></thead><tbody><tr><td>default (&rho; = 0.5)</td><td>66</td><td>61</td><td>21,175</td><td>99.91%</td></tr><tr><td>&rho; = 0.25</td><td>66</td><td>61</td><td>21,175</td><td>99.91%</td></tr><tr><td>&rho; = 1</td><td>66</td><td>61</td><td>20,893 <span class=muted>(−1%)</span></td><td>99.91%</td></tr><tr><td>&rho; = 2</td><td>66</td><td>61</td><td>20,213 <span class=muted>(−5%)</span></td><td>99.89%</td></tr><tr><td>&rho; = 4</td><td>65</td><td>59</td><td>19,560 <span class=muted>(−8%)</span></td><td>99.85%</td></tr></tbody></table></div>
   </section>
 
   <section aria-labelledby="h-how">
@@ -224,7 +224,7 @@ <h2 id="h-limits">Limits of this prototype</h2>
 
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", 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"}, 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", 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5wXlljm2wCKxcOue8RFpJUO2WRvOySjoPgYWPmQrN9owNkFYSVPdQk+m+CsleXs/YwJTcPtoQWEpwZrBHSHXlSsGm1VX27Q5js4VuQ2DhYw9ZZ0d6pXZV6hK1YtYGfx71O5sNjSapXdXZkV4erwisvHm370JcYqeemJZW0i7i7+NZZRXl0rme+Hzy3b4LtAuBJfL4ufD45bNCWrvcmWiX6xkxOyedizl++SwfBwmsPPvAeXloyi2Ri9Foi3W1BhrlWjXKrTqJXMzQlPsD52UahcDKv8MXuyRy4LihyUxz3Ehbq5fIefSHL3bRHASWkMieM4d63stbE2quN6IEh5nzS1dzPbCK1Hk7dOdQz3vsJENgSciF0Sv5mvt+7bQ+dalGX2+kLT5+Ppjx2uFgmXQmX/PaL4xeoS0ILGnp7D6Z3x1vGW7/tCJ1kTavg3qumcnO7pM0BIElRW+eP5bHY3UYwLr5Q1b+hrGCscib54/RBASWRAXnovs+ODqfTuVlJIsBrFudGp2X351Pp/Z9cDQ4F6UJCCwh5e39Xnv/cB4erxw8Xt1cuVWnLtPk/ndfe/8wW/QRWDIwPO35c9ch3gel9FaY64esP3cdGp72UHkCSx4Gve5XTh/I2bthkaqIEXeJvBXOp1OvnD4w6HVTdgJLZs9ZL7+3Pzdj8IYmc1FRETXPe2AFY5GX39vPs1X2qG3CQRWyJByP9U2M1JitJq0+q8PtlWtsklrlK7leXqoJDs2mE2mR5RkM//v+IVkfW0lgFbpEar7b3W8o1daas7V174pvtZcYS69Nj8TNGyIQj/tiIpuzQ18/dySRmqfUBJbsDXhdkXhsSXW9KgsvbtaOWtLqtjILmdCIX2RnneDB7lPv9p+nyDmgoQQiV2t3XDOTj6zecF91A9UQShnGGppyH77YxTpBnrCEIjfPujg2FEvM1VfUaNTqRXzCora3pVKrImOhVGx+EXfjO9J75vDFM2xxRWAp2UTA96GrT6NW2y3VBFYulVdp/Vd8YpF2On797NH8Lh3llRC5e9R6u+f0h6N9G1va2uqXUpDcKKvUisU48+bMYA/7shNYogDPsHjz/Ikzgz1rHSvbHcspiJD86afnnJeIKgKr0GOrs/vksSvn7m9YtnPVuiLB9z5p8YZmL7oHP3L3M1ZFYOH6S+KZoe5ijXpoaqy1tnmZrbHKwNrAfPKFA/1eV9/ECAuYCSyIWx0aJoQY908fu/R+pd7sqKptrKyxW2xZnSgPccPCGo/f65qZdPommKlAYEHc1T7xM5HA1XNWdGVam8FSZTCbdUZzuUFfptWWlJUWlxSrNWoVq0HF3c7znE+nEvPJWDIeiccCc+FANOQLB7xhfzQeoz4SV7RGbKcKsvby//0XI193IiMyz335h9RBsFsD8vnIkFqgCBSKwII8JBMJikChCCzIQyzK53YKRWBBJkKhMEWgUAQW5CEwG6IIFIrAgjxMT89QBApFYEEepiaYik2hCCzIhGeMTU4oFIEFmXA5xykChSKwIA9Bf9A3xZ4nt+Gbmg36g9SBwEL+DfQNUwRKRGBBHvp6BykCJSKwIA+9PX0UgRIRWJCH8TGvx80nsFvyuCfHx7zUgcCCVJw/200RKA6BBXk4d/pDikBxCCzIw2C/c3TEQx0+bXTEM9jvpA4EFqTl1IkuikBZCCzIw4mjpzMiQx3Ex7dFPnH0NHUgsCA5kVD02MGT1OFGxw6ejISi1IHAghS93fkORaAgBBbkwT3qOXmMIZu/OXmsyz3KhwgCCxL21htHKAKlILAgm4esQ/uPU4dD+4/zeEVgQQb2vbo/Fo0V9gE5sX2v7qcnEFgQsvhc+JeX3izkCvzlpTf5OEhgQTYOH3jno/O9hXnvH53vPXyAj4MEFmTl5RdfS6XShXbXqVT65Rdfo/WVSm0TDqqgSOFQNBgId6xvK6i7/uPzr/ZcuEzrE1iQH+eQy2IxNrc0Fcj9/vXgu/v+3Em780oIuXrx+VcuF8buwJd7B198/hVanMCCvP3ht39S/LE6vqnZP/z2T7Q1gQX5/2Oe9v/u1y8k4kml3mAinvzdr1/wTftpa8EYFhRgdiYwPDS6Zfv6IlEkFLeBzG9+9fwVDsUhsKAkU5O+4UHX5m1rlZRZGZH5zT8/333+Eu1LYEFpJsenBvpG1m1s12jUyngT/M2vSCsCC4p+zurtvtL2wAqtrlzIfJT933/1e94ECSwIxY9nnTtzwXFfk7W6Qsh2BsN//MvvxziKkcBCIYjF4u8cPS3TOaV/Pfjuf/7bf8dicdqRwEIBuXDuon82uKZjpUqlkss6wT8+/ypz2QksiIJdu/P+e+dttdaa2moh+T0Yfvuvf2CdIIFFYIkCXyN96sTZcDC8bMWS4pJiae7G9z9/fP2lF14Ls8VVwStaI7ZTBQgh9Ebdk8/u3b13u9R2Ot736n524wOBhZtoaLI//pUvbN2xQUjgzJu33jjCvuwgsHD72Hr0sYd37Nma+2nxGZE5dvDk253vEFUgsHB3L4nbdm3asm1DU7M9Bz83OuI5daLrxNHTvACCwMK9a1nmWLvpgY51a+wNNYv+xz3uyfNnu8+d/nCw30mpQWBh0dTV21a1tbaualnauqTqc0yU903NDvQN9/UO9vb0jY95KSwILGSXyWJqdNTZ62uqa61Wa6W5wmg0GrS6spLSUrVGJYRIpxaSiUQsGg+FwoHZ0PT0zNTEtGds0uUcD/qDFBAEFgDBjqMAQGABAIEFgMACAAILAAgsAAQWABBYAEBgASCwAIDAAgACCwCBBQAEFgAQWAAILAAgsAAQWABAYAEAgQWAwAIAAgsACCwABBYAEFgAQGABILAAgMACAAILAIEFAAQWABBYAAgsACCwAIDAAkBgAQCBBQAEFgACCwAILAAgsAAQWABAYAEAgQWAwAIAAgsAgQUABBYAEFgACtH/A05WORi9WQojAAAAAElFTkSuQmCC"}, 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", 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", 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", 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"}, 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", 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", 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+5zdaczWYZGZTY9Vme3X0P7/ZXp3CSWYkk4V9mGDpKVj5uSn89BdtRVE2a6M1V7PpZbavtkfZrM326tROLyMl84oeBPNhpd24F+aldgVetBUtNJrunsBPfO66lfYT+G1fbW8uzrt7Aj/xuetW6TCBn5SVwz5MsPR0hGXLT/k6bLBaN1it6TZF8qbGqk2NVWk+RbKUTbAIlp4Yim1psibrc6ypnb5dTPK9YZq06d7BsuayDwuuYenoH4oSG4OQuaQcgkWwdHWENas4tdfdMY0LWNlyXj7jQLD0deOosaqMccjIYOUXcqc7wdIdgpWpvzMFnM4TLB0Gq7qCQcjI35nCIgaBYOmOqaaSQcjIU0JbMYNAsPR3hDWnysB3hRlXK2ueTLAIltDj44TZpnmzGYcM+4UpLpXMPEhIsPR5Vlg3h0HIsF+YklkMguBOd30yz69N+Tqk26M54i9vKkzPR3PkUr7bTekpeaC9hVFIlbDH1/PjX0zc6EnVO+uT9/BzxboFIt531qftw8+yrdi0/lnJyE1YnBLqc/SzzObG+pR89EvDQy8MD07xFuj+UPCF4cGXhoe0XKttLW1bD5yf4i3QvW7f1gPnt7W0pWZ7lVVSK4KlaxZ7fUpqtcM5Hs1P7nCOa9asbS1tr7V1RfOTr7V1paRZSnkVeyzB0newFs03Vs7S+EwwylrdadZbTmey1+r19u4oa3WnWa+3d2v6q5JfKBMsgiX0PpNfvqWpUctPfGXUocEisXr5xBUNFpnWr0rlbKbBIlgQWUsatfxOcIrrVmLy61k7Xc6kfifYG/tL53vdvjc6tPu+QqnkpjmCBSGymhvNDfO0+ayDHo/GC0ZjT+eAxgvG/Hsyq0KuqmFfJVgQcpYle/kibT7rgn9C4wWj0TowpvGCMR9eza6VjCb2VYIFIYTIfmCxWqjFnHCf3h2q5YLR6Iv9fHCaC4oYZ3BXZteylxIsfMY0ryZ7ZRPjkJ6UmrlyMU/kECx8/iBrZbOkKMn+lBJV1XjBaJRlmzVeMJZfEVmpqWP/JFj4YrBWNGWvWprsT1kY74WYhcm8gtNckqfxgjEcXtXWKzVz2T8JFr7M+sjyZH/EmnhfWbwmme86XltTovGC0VPnzmfPJFi4h5xHHshJcrPW51hLlZhP7koVNamTNzzbUFke+8ldebY52ZM3KHPnKwSLYOHeJMn66AohJ3ejbMkv0GCRWD2/ok6DRWLdHOq8BUKS2DEJFiY5yHr0gdzHHhRJfkP9xlheXLzRmhv3JDMiljfUb7ZXR//zm+3VyZ5kRq1vVOYtYJ8kWJiK9bGHkv2O1RdtRVE2a6M190WbRi+J2b7aHmWzNturt6+2J/foypKl1C9kb0w3ygtbvscopBVDWUlodNzXcVUk+ep7qaK2+/3uSFhMct3qJ4W2f473ZDC3vii+i+gVOeazg+POQHCy61a/fKThJw8k/T4Dg71ZXdTM3iiYcRT3NdHZM7D9P31XOkXGTpEc94yjIg2mSJZLZhkff5K34xAsRGts1/6BV/47c9d/msFKLePqJ1Q7h1eCa1iI+pTqia/kfvVhxkF76ny72rCYcSBYiOXQ16Dmrl1trC5nKLR9Db1NXdgkkv+MFAjWTGNZWJe37nHGQdPDK/tSuayScSBYiEf+U4/nPfkY46BVrZZw6YpgYVo3W+c/9XhWUwMjkWxKxWzVvpT72gkWpsVYU5n/9NcMpUUMRRL/XbDmqU3LuI+BYCEBsh9szn/mCUlhYyUpV7JhyQPKHCa9IlhIkPynv5a/fi3jkAyG5hXq4mWMA8FCIhV86+u5TzzKOCSW2rBYbV7BOBAsJJiSm1Pwd0/mrOJYIJGziRqWrpTMFoaCYCHxjBWzCjc+lbVsEUORgFpVzzEse0jKL2QoCBaSxVRXU/j3T1kWM0/TNG9iqDYsXyWX8C4cggWR7Dvg623ffdqyiGbFu9NXVKsPPMwd7ZlIZQgyslmLF9gkMfKm5DnbwWjEdmxVOduwfJVcUc1QcIQFDZu1aIFt0/rsFbx+NbZXohpWPkqtOMJCCpgb5tk2fUs2m52HjjMa969VXYNh6YNyUQlDQbCQGqbaKtv3Nsi52WPv72c0ptrRFzUbmldK1jyGgmAhlQylRUWbv63m5zp27g17vAzIl0hGk7rkAcOSFcJgYDQIFlJPNpsKv/u0WlToeGefv7OHAfnryNiK1ablzCBKsCDSb1blRw2zikff/dD18RlGQwih1Napi5crXGInWBDperuDoazEWFU2uvtA2OXR72mgyazal6j2pVKOlb2CYCGNt2hxoe0fNxjnVI3tPuA9f0mPp4HlVQZ7s1LHrIcECxnCunqlad7s8b2HxvceCjndOjqwamxSGxfzhCDBgsi4J6WLNn/b0lg3/uERPVzVUubUqQ2LmIePYEFk9GylFvt8S1OD88DHvkvXZ+Y5YGm5Wt+ozF8omcxscYKFDP99zsnK/+bfZC9f5Dx43Nly3N/dO3P+agU2pa5BrW+U8grY0LyqHjPNxI1u15FTro9O+bt6M/pV9XJhkVJbr8ybL9t41IYjLMxQpjlVpjlV1jUrXUfPuI+1ZuJJolxapsypU2rr5QIbG5QjLOhFcGTMc/Ks++Q596lzEX8g3Y+wFFWZXavMnqvUzJWystl8BAs65W275Dnd5mm94Lt8Iw2DJZeWKZU1SvUcubyKjUWwCBaEECLs9XnPXfS2XfJeuOK7eC3lwZJLy5XySrm8SqmoFgYjGwiCa1j4ayAs5uyVS7JXLokEQ76L13wXr/kuXZ+42hnoH9LyDcxyySy5tEwpLZdnVQiZCSZBsDB1NVTFYq+32OuFEKFxl/9G90Rnj//mLX93r7+7LzTuSuRnmS1SgU0usMmFRVJRsWwr4aVbIFgQcb8M0dLUYGn67Lm8sNsT6B0IDAwFB0aCw46gYzTkGA853WGXO+ydCPt8kWBIhMOfC5IkFEVSDcJglMxmyWSRsrKkrBwpJ0fKyZWseVJegWQyMc7gGhYAXkIBAAQLAAgWAIIFAAQLAAgWAIIFAAQLAAgWAIIFAAQLAAgWAIIFAAQLAMECAIIFAAQLAMECAIIFAAQLAMECAIIFAAQLAMECAIIFAAQLAMECAIIFAAQLAMECAIIFAAQLAMECAIIFAAQLAMECAIIFAAQLAMECAIIFAAQLAMECAIIFgGABAMECAIIFgGABAMECAIIFgGABAMECAIIFgGABAMECAIIFgGABAMECAIIFgGABQEr8P8JBov86qqcwAAAAAElFTkSuQmCC", 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"}, 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@@ -7985,17 +7528,17 @@
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@@ -8257,17 +7800,17 @@
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@@ -8461,17 +8004,17 @@
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@@ -10376,7 +9783,7 @@
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   "sim_match": 99.49583333333332,
-  "seconds": 63.1,
+  "seconds": 129.8,
   "match": 99.9,
   "exact": 99.75,
   "human_len": 359,
@@ -10386,17 +9793,17 @@
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-  "code": "<body bgcolor=90d3a6><b style=\"border-radius:0 50% 50% 0;box-shadow:0 170px #194e29\"><b style=\"left:95;border-radius:50% 0 0 50%;box-shadow:0 170px #194e29\"><b style=top:135;width:126><b style=left:0;top:135;width:125><b style=left:125;width:150;height:200><b style=\"left:155;top:80;height:140;background:#90d3a6;box-shadow:60px 0 #90d3a6\"><style>b{position:fixed;width:30;height:30;background:#194e29;left:275;top:50",
-  "elements": 6,
-  "shadows": 3,
+  "chars": 414,
+  "code": "<body bgcolor=90d3a6><b style=\"left:275;border-radius:0 50% 50% 0;box-shadow:0 170px #194e29\"><b style=\"left:95;border-radius:50% 0 0 50%;box-shadow:0 170px #194e29\"><b style=\"left:0;top:135;padding:15 63;box-shadow:275px 0 #194e29\"><b style=\"left:125;padding:100 75\"><b style=\"left:155;top:80;padding:70 15;background:#90d3a6;box-shadow:60px 0 #90d3a6\"><style>b{position:fixed;padding:15;background:#194e29;top:50",
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+  "shadows": 4,
   "shapes": 9,
   "grouping": "exact",
-  "greedy_chars": 417,
-  "exact_chars": 417,
+  "greedy_chars": 414,
+  "exact_chars": 414,
   "exact_optimal": true,
   "wrong_px": 32,
-  "J": 481.0,
+  "J": 478.0,
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@@ -10504,7 +9911,7 @@
     {
      "x": 0,
      "y": 135,
-     "w": 125,
+     "w": 126,
      "h": 30,
      "color": 1,
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@@ -10569,7 +9976,7 @@
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   "sim_match": 99.97333333333333,
-  "seconds": 127.8,
+  "seconds": 243.9,
   "match": 99.93,
   "exact": 99.8,
   "human_len": 748,
diff --git a/bench/tradeoff.json b/bench/tradeoff.json
index d143df3..d269174 100644
--- a/bench/tradeoff.json
+++ b/bench/tradeoff.json
@@ -110,8 +110,8 @@
  {
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   "rho": 0.0,
-  "chars": 417,
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+  "code": "<body bgcolor=6592cf><i style=left:50></i><i style=left:300;scale:-1></i><b style=left:125;top:75;padding:75;background:#243d83><b style=left:175;top:125;padding:25;background:#eeb850><style>b{position:fixed;border-radius:50%}i{position:fixed;padding:75 25;background:#243d83;top:75;clip-path:polygon(0 0,50px 0,84% 8%,80% 16px,64% 32px,50% 62px,50% 54%,54% 100px,68% 82%,84% 92%,50px 100%,0 100%)",
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   "png": "bench/tradeoff/ks-03-pushbutton__0.0.png",
   "match": 99.83
@@ -119,8 +119,8 @@
  {
   "id": "ks-03-pushbutton",
   "rho": 0.25,
-  "chars": 417,
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+  "code": "<body bgcolor=6592cf><i style=left:50></i><i style=left:300;scale:-1></i><b style=left:125;top:75;padding:75;background:#243d83><b style=left:175;top:125;padding:25;background:#eeb850><style>b{position:fixed;border-radius:50%}i{position:fixed;padding:75 25;background:#243d83;top:75;clip-path:polygon(0 0,50px 0,84% 8%,80% 16px,64% 32px,50% 62px,50% 54%,54% 100px,68% 82%,84% 92%,50px 100%,0 100%)",
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   "png": "bench/tradeoff/ks-03-pushbutton__0.25.png",
   "match": 99.83
@@ -128,8 +128,8 @@
  {
   "id": "ks-03-pushbutton",
   "rho": 0.5,
-  "chars": 417,
-  "code": "<body bgcolor=6592cf><i style=\"inset:75px 300px 75px 50px\"></i><i style=\"inset:75px 50px 75px 300px;scale:-1\"></i><b style=left:125;top:75;padding:75;background:#243d83><b style=left:175;top:125;padding:25;background:#eeb850><style>b{position:fixed;border-radius:50%}i{position:fixed;background:#243d83;clip-path:polygon(0 0,50px 0,84% 8%,80% 16px,64% 32px,50% 62px,50% 54%,54% 100px,68% 82%,84% 92%,50px 100%,0 100%)",
+  "chars": 397,
+  "code": "<body bgcolor=6592cf><i style=left:50></i><i style=left:300;scale:-1></i><b style=left:125;top:75;padding:75;background:#243d83><b style=left:175;top:125;padding:25;background:#eeb850><style>b{position:fixed;border-radius:50%}i{position:fixed;padding:75 25;background:#243d83;top:75;clip-path:polygon(0 0,50px 0,84% 8%,80% 16px,64% 32px,50% 62px,50% 54%,54% 100px,68% 82%,84% 92%,50px 100%,0 100%)",
   "shapes": 4,
   "png": "bench/tradeoff/ks-03-pushbutton__0.5.png",
   "match": 99.83
@@ -137,8 +137,8 @@
  {
   "id": "ks-03-pushbutton",
   "rho": 1.0,
-  "chars": 417,
-  "code": "<body bgcolor=6592cf><i style=\"inset:75px 300px 75px 50px\"></i><i style=\"inset:75px 50px 75px 300px;scale:-1\"></i><b style=left:125;top:75;padding:75;background:#243d83><b style=left:175;top:125;padding:25;background:#eeb850><style>b{position:fixed;border-radius:50%}i{position:fixed;background:#243d83;clip-path:polygon(0 0,50px 0,84% 8%,80% 16px,64% 32px,50% 62px,50% 54%,54% 100px,68% 82%,84% 92%,50px 100%,0 100%)",
+  "chars": 397,
+  "code": "<body bgcolor=6592cf><i style=left:50></i><i style=left:300;scale:-1></i><b style=left:125;top:75;padding:75;background:#243d83><b style=left:175;top:125;padding:25;background:#eeb850><style>b{position:fixed;border-radius:50%}i{position:fixed;padding:75 25;background:#243d83;top:75;clip-path:polygon(0 0,50px 0,84% 8%,80% 16px,64% 32px,50% 62px,50% 54%,54% 100px,68% 82%,84% 92%,50px 100%,0 100%)",
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   "png": "bench/tradeoff/ks-03-pushbutton__1.0.png",
   "match": 99.83
@@ -146,8 +146,8 @@
  {
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   "rho": 2.0,
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+  "chars": 397,
+  "code": "<body bgcolor=6592cf><i style=left:50></i><i style=left:300;scale:-1></i><b style=left:125;top:75;padding:75;background:#243d83><b style=left:175;top:125;padding:25;background:#eeb850><style>b{position:fixed;border-radius:50%}i{position:fixed;padding:75 25;background:#243d83;top:75;clip-path:polygon(0 0,50px 0,84% 8%,80% 16px,64% 32px,50% 62px,50% 54%,54% 100px,68% 82%,84% 92%,50px 100%,0 100%)",
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   "png": "bench/tradeoff/ks-03-pushbutton__2.0.png",
   "match": 99.83
@@ -155,8 +155,8 @@
  {
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   "rho": 4.0,
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+  "chars": 397,
+  "code": "<body bgcolor=6592cf><i style=left:50></i><i style=left:300;scale:-1></i><b style=left:125;top:75;padding:75;background:#243d83><b style=left:175;top:125;padding:25;background:#eeb850><style>b{position:fixed;border-radius:50%}i{position:fixed;padding:75 25;background:#243d83;top:75;clip-path:polygon(0 0,50px 0,84% 8%,80% 16px,64% 32px,50% 62px,50% 54%,54% 100px,68% 82%,84% 92%,50px 100%,0 100%)",
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   "png": "bench/tradeoff/ks-03-pushbutton__4.0.png",
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@@ -218,8 +218,8 @@
  {
   "id": "ks-05-acidrains",
   "rho": 0.0,
-  "chars": 266,
-  "code": "<body bgcolor=0b2429><b style=left:200;top:30;border-radius:50%><b style=\"left:141;top:90;width:119;background:#998235;border-radius:58px 0 60px 0\"><b style=\"left:80;top:150;border-radius:50% 0 50% 50%\"><style>b{position:fixed;width:120;height:120;background:#f3ac3c",
+  "chars": 263,
+  "code": "<body bgcolor=0b2429><b style=left:200;top:30;padding:60;border-radius:50%><b style=\"inset:90px 140px 91px;background:#998235;border-radius:60px 0 58px 0\"><b style=\"left:80;top:150;padding:60;border-radius:50% 0 50% 50%\"><style>b{position:fixed;background:#f3ac3c",
   "shapes": 3,
   "png": "bench/tradeoff/ks-05-acidrains__0.0.png",
   "match": 99.91
@@ -227,8 +227,8 @@
  {
   "id": "ks-05-acidrains",
   "rho": 0.25,
-  "chars": 266,
-  "code": "<body bgcolor=0b2429><b style=left:200;top:30;border-radius:50%><b style=\"left:141;top:90;width:119;background:#998235;border-radius:58px 0 60px 0\"><b style=\"left:80;top:150;border-radius:50% 0 50% 50%\"><style>b{position:fixed;width:120;height:120;background:#f3ac3c",
+  "chars": 263,
+  "code": "<body bgcolor=0b2429><b style=left:200;top:30;padding:60;border-radius:50%><b style=\"inset:90px 140px 91px;background:#998235;border-radius:60px 0 58px 0\"><b style=\"left:80;top:150;padding:60;border-radius:50% 0 50% 50%\"><style>b{position:fixed;background:#f3ac3c",
   "shapes": 3,
   "png": "bench/tradeoff/ks-05-acidrains__0.25.png",
   "match": 99.91
@@ -236,8 +236,8 @@
  {
   "id": "ks-05-acidrains",
   "rho": 0.5,
-  "chars": 266,
-  "code": "<body bgcolor=0b2429><b style=left:200;top:30;border-radius:50%><b style=\"left:141;top:90;width:119;background:#998235;border-radius:58px 0 60px 0\"><b style=\"left:80;top:150;border-radius:50% 0 50% 50%\"><style>b{position:fixed;width:120;height:120;background:#f3ac3c",
+  "chars": 263,
+  "code": "<body bgcolor=0b2429><b style=left:200;top:30;padding:60;border-radius:50%><b style=\"inset:90px 140px 91px;background:#998235;border-radius:60px 0 58px 0\"><b style=\"left:80;top:150;padding:60;border-radius:50% 0 50% 50%\"><style>b{position:fixed;background:#f3ac3c",
   "shapes": 3,
   "png": "bench/tradeoff/ks-05-acidrains__0.5.png",
   "match": 99.91
@@ -245,8 +245,8 @@
  {
   "id": "ks-05-acidrains",
   "rho": 1.0,
-  "chars": 266,
-  "code": "<body bgcolor=0b2429><b style=left:200;top:30;border-radius:50%><b style=\"left:141;top:90;width:119;background:#998235;border-radius:58px 0 60px 0\"><b style=\"left:80;top:150;border-radius:50% 0 50% 50%\"><style>b{position:fixed;width:120;height:120;background:#f3ac3c",
+  "chars": 263,
+  "code": "<body bgcolor=0b2429><b style=left:200;top:30;padding:60;border-radius:50%><b style=\"inset:90px 140px 91px;background:#998235;border-radius:60px 0 58px 0\"><b style=\"left:80;top:150;padding:60;border-radius:50% 0 50% 50%\"><style>b{position:fixed;background:#f3ac3c",
   "shapes": 3,
   "png": "bench/tradeoff/ks-05-acidrains__1.0.png",
   "match": 99.91
@@ -255,7 +255,7 @@
   "id": "ks-05-acidrains",
   "rho": 2.0,
   "chars": 245,
-  "code": "<body bgcolor=0b2429><b style=left:200;top:30;border-radius:50%><b style=\"left:140;top:90;background:#998235;border-radius:58px 0 50% 0\"><b style=\"left:80;top:150;border-radius:50% 0 50% 50%\"><style>b{position:fixed;padding:60;background:#f3ac3c",
+  "code": "<body bgcolor=0b2429><b style=left:200;top:30;border-radius:50%><b style=\"left:140;top:90;background:#998235;border-radius:50% 0 58px 0\"><b style=\"left:80;top:150;border-radius:50% 0 50% 50%\"><style>b{position:fixed;padding:60;background:#f3ac3c",
   "shapes": 3,
   "png": "bench/tradeoff/ks-05-acidrains__2.0.png",
   "match": 99.87
@@ -264,7 +264,7 @@
   "id": "ks-05-acidrains",
   "rho": 4.0,
   "chars": 245,
-  "code": "<body bgcolor=0b2429><b style=left:200;top:30;border-radius:50%><b style=\"left:140;top:90;background:#998235;border-radius:58px 0 50% 0\"><b style=\"left:80;top:150;border-radius:50% 0 50% 50%\"><style>b{position:fixed;padding:60;background:#f3ac3c",
+  "code": "<body bgcolor=0b2429><b style=left:200;top:30;border-radius:50%><b style=\"left:140;top:90;background:#998235;border-radius:50% 0 58px 0\"><b style=\"left:80;top:150;border-radius:50% 0 50% 50%\"><style>b{position:fixed;padding:60;background:#f3ac3c",
   "shapes": 3,
   "png": "bench/tradeoff/ks-05-acidrains__4.0.png",
   "match": 99.87
@@ -326,62 +326,62 @@
  {
   "id": "ks-07-leafytrial",
   "rho": 0.0,
-  "chars": 289,
-  "code": "<body bgcolor=0b2429><b style=\"left:75;width:140;background:#1a4341;border-radius:99px 0 0 0\"><b style=\"left:125;width:183;background:#998235;border-radius:99px 0 77px 0\"><b style=\"left:175;width:150;background:#f3ac3c;border-radius:66% 0 100px 0\"><style>b{position:fixed;height:150;top:75",
+  "chars": 291,
+  "code": "<body bgcolor=0b2429><b style=\"left:75;width:140;background:#1a4341;border-radius:99px 0 0 0\"><b style=\"left:125;width:193;background:#998235;border-radius:99px 0 88.5px 0\"><b style=\"left:175;width:150;background:#f3ac3c;border-radius:66% 0 100px 0\"><style>b{position:fixed;height:150;top:75",
   "shapes": 3,
   "png": "bench/tradeoff/ks-07-leafytrial__0.0.png",
-  "match": 99.82
+  "match": 99.81
  },
  {
   "id": "ks-07-leafytrial",
   "rho": 0.25,
-  "chars": 289,
-  "code": "<body bgcolor=0b2429><b style=\"left:75;width:140;background:#1a4341;border-radius:99px 0 0 0\"><b style=\"left:125;width:183;background:#998235;border-radius:99px 0 77px 0\"><b style=\"left:175;width:150;background:#f3ac3c;border-radius:66% 0 100px 0\"><style>b{position:fixed;height:150;top:75",
+  "chars": 291,
+  "code": "<body bgcolor=0b2429><b style=\"left:75;width:140;background:#1a4341;border-radius:99px 0 0 0\"><b style=\"left:125;width:193;background:#998235;border-radius:99px 0 88.5px 0\"><b style=\"left:175;width:150;background:#f3ac3c;border-radius:66% 0 100px 0\"><style>b{position:fixed;height:150;top:75",
   "shapes": 3,
   "png": "bench/tradeoff/ks-07-leafytrial__0.25.png",
-  "match": 99.82
+  "match": 99.81
  },
  {
   "id": "ks-07-leafytrial",
   "rho": 0.5,
-  "chars": 289,
-  "code": "<body bgcolor=0b2429><b style=\"left:75;width:140;background:#1a4341;border-radius:99px 0 0 0\"><b style=\"left:125;width:183;background:#998235;border-radius:99px 0 77px 0\"><b style=\"left:175;width:150;background:#f3ac3c;border-radius:66% 0 100px 0\"><style>b{position:fixed;height:150;top:75",
+  "chars": 291,
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   "shapes": 3,
   "png": "bench/tradeoff/ks-07-leafytrial__0.5.png",
-  "match": 99.82
+  "match": 99.81
  },
  {
   "id": "ks-07-leafytrial",
   "rho": 1.0,
-  "chars": 289,
-  "code": "<body bgcolor=0b2429><b style=\"left:75;width:140;background:#1a4341;border-radius:99px 0 0 0\"><b style=\"left:125;width:183;background:#998235;border-radius:99px 0 77px 0\"><b style=\"left:175;width:150;background:#f3ac3c;border-radius:66% 0 100px 0\"><style>b{position:fixed;height:150;top:75",
+  "chars": 291,
+  "code": "<body bgcolor=0b2429><b style=\"left:75;width:140;background:#1a4341;border-radius:99px 0 0 0\"><b style=\"left:125;width:193;background:#998235;border-radius:99px 0 88.5px 0\"><b style=\"left:175;width:150;background:#f3ac3c;border-radius:66% 0 100px 0\"><style>b{position:fixed;height:150;top:75",
   "shapes": 3,
   "png": "bench/tradeoff/ks-07-leafytrial__1.0.png",
-  "match": 99.82
+  "match": 99.81
  },
  {
   "id": "ks-07-leafytrial",
   "rho": 2.0,
   "chars": 289,
-  "code": "<body bgcolor=0b2429><b style=\"left:75;width:140;background:#1a4341;border-radius:99px 0 0 0\"><b style=\"left:125;width:183;background:#998235;border-radius:99px 0 77px 0\"><b style=\"left:175;width:150;background:#f3ac3c;border-radius:66% 0 100px 0\"><style>b{position:fixed;height:150;top:75",
+  "code": "<body bgcolor=0b2429><b style=\"left:75;padding:75 70;background:#1a4341;border-radius:99px 0 0 0\"><b style=\"left:125;padding:75 97;background:#998235;border-radius:99px 0 88.5px 0\"><b style=\"left:175;padding:75;background:#f3ac3c;border-radius:66% 0 100px 0\"><style>b{position:fixed;top:75",
   "shapes": 3,
   "png": "bench/tradeoff/ks-07-leafytrial__2.0.png",
-  "match": 99.82
+  "match": 99.79
  },
  {
   "id": "ks-07-leafytrial",
   "rho": 4.0,
   "chars": 289,
-  "code": "<body bgcolor=0b2429><b style=\"left:75;width:140;background:#1a4341;border-radius:99px 0 0 0\"><b style=\"left:125;width:183;background:#998235;border-radius:99px 0 77px 0\"><b style=\"left:175;width:150;background:#f3ac3c;border-radius:66% 0 100px 0\"><style>b{position:fixed;height:150;top:75",
+  "code": "<body bgcolor=0b2429><b style=\"left:75;padding:75 70;background:#1a4341;border-radius:99px 0 0 0\"><b style=\"left:125;padding:75 97;background:#998235;border-radius:99px 0 88.5px 0\"><b style=\"left:175;padding:75;background:#f3ac3c;border-radius:66% 0 100px 0\"><style>b{position:fixed;top:75",
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   "png": "bench/tradeoff/ks-07-leafytrial__4.0.png",
-  "match": 99.82
+  "match": 99.79
  },
  {
   "id": "ks-09-tesseract",
   "rho": 0.0,
-  "chars": 370,
-  "code": "<body bgcolor=222730><b style=\"left:0;clip-path:polygon(0 0,98% 0,24% 50%,99% 100%,0 100%)\"></b><b style=\"left:302;clip-path:polygon(0 0,98% 0,98% 100%,0 100%,74% 50%)\"></b><b style=left:125;width:150;rotate:45deg></b><b style=left:175;top:125;width:50;height:50;background:#393e46;border-radius:50%><style>b{position:fixed;width:100;height:150;background:#4caab3;top:75",
+  "chars": 356,
+  "code": "<body bgcolor=222730><b style=\"left:0;clip-path:polygon(0 0,98% 0,24% 50%,99% 100%,0 100%)\"></b><b style=\"left:302;clip-path:polygon(0 0,98% 0,98% 100%,0 100%,74% 50%)\"></b><b style=left:125;padding:75;rotate:45deg></b><b style=left:175;top:125;padding:25;background:#393e46;border-radius:50%><style>b{position:fixed;padding:75 50;background:#4caab3;top:75",
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   "png": "bench/tradeoff/ks-09-tesseract__0.0.png",
   "match": 99.59
@@ -389,8 +389,8 @@
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@@ -767,8 +767,8 @@
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@@ -776,8 +776,8 @@
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@@ -785,8 +785,8 @@
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@@ -794,8 +794,8 @@
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@@ -803,8 +803,8 @@
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@@ -812,62 +812,62 @@
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@@ -875,8 +875,8 @@
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@@ -884,8 +884,8 @@
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@@ -893,8 +893,8 @@
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@@ -902,8 +902,8 @@
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   "rho": 2.0,
-  "chars": 208,
-  "code": "<body bgcolor=e38f66><b style=\"inset:210px 195px 0 195px;background:#62306d\"><b style=\"inset:90px 136px 90px 137px;background:#fffbda;clip-path:polygon(32px 0,94px 0,100% 100%,0 100%)\"><style>b{position:fixed",
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@@ -911,8 +911,8 @@
  {
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   "rho": 4.0,
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-  "code": "<body bgcolor=e38f66><b style=\"inset:210px 195px 0 195px;background:#62306d\"><b style=\"inset:90px 136px 90px 137px;background:#fffbda;clip-path:polygon(32px 0,94px 0,100% 100%,0 100%)\"><style>b{position:fixed",
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@@ -974,44 +974,44 @@
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   "png": "bench/tradeoff/ks-145-spotlight__0.0.png",
-  "match": 99.72
+  "match": 99.73
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   "png": "bench/tradeoff/ks-145-spotlight__0.25.png",
-  "match": 99.72
+  "match": 99.73
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   "png": "bench/tradeoff/ks-145-spotlight__0.5.png",
-  "match": 99.72
+  "match": 99.73
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   "png": "bench/tradeoff/ks-145-spotlight__1.0.png",
-  "match": 99.72
+  "match": 99.73
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@@ -1019,8 +1019,8 @@
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@@ -1082,8 +1082,8 @@
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+  "code": "<body bgcolor=f5d6b4><b style=\"left:261;top:75;padding:10 7\"><b style=\"inset:125px 75px 125px 300px\"><b style=\"inset:25px 175px 225px\"><b style=\"inset:75px 75px 25px;clip-path:polygon(188px 0,100% 85px,60% 75%,60% 100%,40% 100%,40% 75%,20% 75%,20% 50%,0 50%,0 25%,20% 25%,20% 0)\"></b><u style=\"inset:175px 91px 105px 275px;box-shadow:-41px 23px 0 7px #d86f45\"><u style=\"inset:92px 98px 175px 275px\"><style>b{position:fixed;background:#d86f45}u{position:fixed;background:#f5d6b4",
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@@ -1091,8 +1091,8 @@
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@@ -1100,8 +1100,8 @@
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@@ -1109,8 +1109,8 @@
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+  "code": "<body bgcolor=f5d6b4><b style=\"left:261;top:75;padding:10 7\"><b style=\"inset:125px 75px 125px 300px\"><b style=\"inset:25px 175px 225px\"><b style=\"inset:75px 75px 25px;clip-path:polygon(188px 0,100% 85px,60% 75%,60% 100%,40% 100%,40% 75%,20% 75%,20% 50%,0 50%,0 25%,20% 25%,20% 0)\"></b><u style=\"inset:175px 91px 105px 275px;box-shadow:-41px 23px 0 7px #d86f45\"><u style=\"inset:92px 98px 175px 275px\"><style>b{position:fixed;background:#d86f45}u{position:fixed;background:#f5d6b4",
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@@ -1118,8 +1118,8 @@
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+  "code": "<body bgcolor=f5d6b4><b style=\"left:261;top:75;padding:10 7\"><b style=\"inset:125px 75px 125px 300px\"><b style=\"inset:25px 175px 225px\"><b style=\"inset:75px 75px 25px;clip-path:polygon(188px 0,100% 85px,60% 75%,60% 100%,40% 100%,40% 75%,20% 75%,20% 50%,0 50%,0 25%,20% 25%,20% 0)\"></b><u style=\"inset:175px 91px 105px 275px;box-shadow:-41px 23px 0 7px #d86f45\"><u style=\"inset:92px 98px 175px 275px\"><style>b{position:fixed;background:#d86f45}u{position:fixed;background:#f5d6b4",
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@@ -1127,8 +1127,8 @@
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@@ -1136,8 +1136,8 @@
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@@ -1145,8 +1145,8 @@
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@@ -1154,8 +1154,8 @@
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@@ -1163,8 +1163,8 @@
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@@ -1172,8 +1172,8 @@
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@@ -1181,8 +1181,8 @@
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@@ -1298,56 +1298,56 @@
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@@ -1406,62 +1406,62 @@
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@@ -1469,8 +1469,8 @@
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@@ -1478,8 +1478,8 @@
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@@ -1496,62 +1496,62 @@
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@@ -1559,17 +1559,17 @@
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+  "match": 99.65
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@@ -1577,8 +1577,8 @@
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   "png": "bench/tradeoff/ks-186-guernsey__0.25.png",
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@@ -1586,8 +1586,8 @@
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   "png": "bench/tradeoff/ks-186-guernsey__0.5.png",
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@@ -1595,17 +1595,17 @@
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   "png": "bench/tradeoff/ks-186-guernsey__1.0.png",
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@@ -1613,119 +1613,119 @@
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@@ -1784,8 +1784,8 @@
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@@ -1793,8 +1793,8 @@
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@@ -1802,8 +1802,8 @@
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@@ -1811,8 +1811,8 @@
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@@ -1820,8 +1820,8 @@
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@@ -1829,8 +1829,8 @@
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@@ -1838,8 +1838,8 @@
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@@ -1847,8 +1847,8 @@
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@@ -1856,8 +1856,8 @@
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@@ -1865,8 +1865,8 @@
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@@ -1874,8 +1874,8 @@
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@@ -1883,8 +1883,8 @@
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@@ -1892,8 +1892,8 @@
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@@ -1901,8 +1901,8 @@
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@@ -1910,8 +1910,8 @@
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@@ -1919,8 +1919,8 @@
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@@ -1928,8 +1928,8 @@
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@@ -1937,8 +1937,8 @@
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@@ -1946,8 +1946,8 @@
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@@ -1964,8 +1964,8 @@
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@@ -1973,8 +1973,8 @@
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@@ -1982,8 +1982,8 @@
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@@ -1991,8 +1991,8 @@
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@@ -2000,8 +2000,8 @@
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@@ -2009,8 +2009,8 @@
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@@ -2018,8 +2018,8 @@
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@@ -2027,8 +2027,8 @@
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@@ -2036,8 +2036,8 @@
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@@ -2045,8 +2045,8 @@
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@@ -2054,8 +2054,8 @@
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@@ -2063,8 +2063,8 @@
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+  "code": "<body bgcolor=9ed7f8><b style=\"inset:90px 50px;background:#277292;border-radius:20px\"><b style=\"inset:140px 190px;border-radius:50%;box-shadow:35px 0 #fff,70px 0 #fff,0 -35px #fff,35px -35px #fff,70px -35px #fff,-70px -35px #fff,-35px -35px #fff,-70px 0 #fff,-35px 0 #fff\"><b style=\"inset:105px 295px 175px 65px;box-shadow:0 35px #fff,0 70px #fff,230px 0 #fff,230px 35px #fff,230px 70px #fff\"><b style=\"inset:175px 120px 105px\"><style>b{position:fixed;background:#fff;border-radius:1em",
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@@ -2072,8 +2072,8 @@
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+  "code": "<body bgcolor=9ed7f8><b style=\"inset:90px 50px;background:#277292;border-radius:20px\"><b style=\"inset:140px 190px;border-radius:50%;box-shadow:35px 0 #fff,70px 0 #fff,0 -35px #fff,35px -35px #fff,70px -35px #fff,-70px -35px #fff,-35px -35px #fff,-70px 0 #fff,-35px 0 #fff\"><b style=\"inset:105px 295px 175px 65px;box-shadow:0 35px #fff,0 70px #fff,230px 0 #fff,230px 35px #fff,230px 70px #fff\"><b style=\"inset:175px 120px 105px\"><style>b{position:fixed;background:#fff;border-radius:1em",
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@@ -2081,8 +2081,8 @@
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+  "code": "<body bgcolor=9ed7f8><b style=\"inset:90px 50px;background:#277292;border-radius:20px\"><b style=\"inset:140px 190px;border-radius:50%;box-shadow:35px 0 #fff,70px 0 #fff,0 -35px #fff,35px -35px #fff,70px -35px #fff,-70px -35px #fff,-35px -35px #fff,-70px 0 #fff,-35px 0 #fff\"><b style=\"inset:105px 295px 175px 65px;box-shadow:0 35px #fff,0 70px #fff,230px 0 #fff,230px 35px #fff,230px 70px #fff\"><b style=\"inset:175px 120px 105px\"><style>b{position:fixed;background:#fff;border-radius:1em",
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@@ -2090,8 +2090,8 @@
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+  "code": "<body bgcolor=9ed7f8><b style=\"inset:90px 50px;background:#277292;border-radius:20px\"><b style=\"inset:140px 190px;border-radius:50%;box-shadow:35px 0 #fff,70px 0 #fff,0 -35px #fff,35px -35px #fff,70px -35px #fff,-70px -35px #fff,-35px -35px #fff,-70px 0 #fff,-35px 0 #fff\"><b style=\"inset:105px 295px 175px 65px;box-shadow:0 35px #fff,0 70px #fff,230px 0 #fff,230px 35px #fff,230px 70px #fff\"><b style=\"inset:175px 120px 105px\"><style>b{position:fixed;background:#fff;border-radius:1em",
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@@ -2099,8 +2099,8 @@
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+  "code": "<body bgcolor=9ed7f8><b style=\"inset:90px 50px;background:#277292;border-radius:20px\"><b style=\"inset:140px 190px;border-radius:50%;box-shadow:35px 0 #fff,70px 0 #fff,0 -35px #fff,35px -35px #fff,70px -35px #fff,-70px -35px #fff,-35px -35px #fff,-70px 0 #fff,-35px 0 #fff\"><b style=\"inset:105px 295px 175px 65px;box-shadow:0 35px #fff,0 70px #fff,230px 0 #fff,230px 35px #fff,230px 70px #fff\"><b style=\"inset:175px 120px 105px\"><style>b{position:fixed;background:#fff;border-radius:1em",
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@@ -2108,8 +2108,8 @@
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@@ -2576,8 +2576,8 @@
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@@ -2612,8 +2612,8 @@
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@@ -2621,8 +2621,8 @@
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@@ -2630,8 +2630,8 @@
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@@ -2639,8 +2639,8 @@
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@@ -2657,8 +2657,8 @@
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@@ -2666,8 +2666,8 @@
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@@ -2675,8 +2675,8 @@
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@@ -2684,8 +2684,8 @@
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@@ -2693,8 +2693,8 @@
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@@ -2702,8 +2702,8 @@
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@@ -2720,8 +2720,8 @@
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@@ -2882,8 +2882,8 @@
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@@ -2891,8 +2891,8 @@
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@@ -2900,8 +2900,8 @@
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@@ -3089,8 +3089,8 @@
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-  "code": "<body bgcolor=512b2a><b style=left:135;width:130;height:180;background:#ea6b62><b style=\"top:125;height:50;box-shadow:-130px 0 #ea6b62,130px 0 #ea6b62\"><b style=top:215;height:26><b style=height:25><style>b{position:fixed;width:50;background:#512b2a;left:175;top:60",
+  "chars": 244,
+  "code": "<body bgcolor=512b2a><b style=\"inset:60px 135px;background:#ea6b62\"><b style=\"inset:125px 175px;box-shadow:130px 0 #ea6b62,-130px 0 #ea6b62\"><b style=\"inset:60px 175px 215px;box-shadow:0 155px #512b2a\"><style>b{position:fixed;background:#512b2a",
   "shapes": 6,
   "png": "bench/tradeoff/om-20240331__4.0.png",
   "match": 100.0
@@ -3458,8 +3458,8 @@
  {
   "id": "om-20240401",
   "rho": 0.0,
-  "chars": 217,
-  "code": "<body bgcolor=0a6190><b style=\"position:fixed;inset:50px 55px 50px 55px;background:#eff8fe;clip-path:polygon(50% 0,220px 40%,271px 40%,100% 50%,271px 60%,220px 60%,50% 100%,70px 60%,19px 60%,0 50%,20px 40%,70px 40%)\">",
+  "chars": 207,
+  "code": "<body bgcolor=0a6190><b style=\"position:fixed;inset:50px 55px;background:#eff8fe;clip-path:polygon(50% 0,220px 40%,271px 40%,100% 50%,271px 60%,220px 60%,50% 100%,70px 60%,19px 60%,0 50%,20px 40%,70px 40%)\">",
   "shapes": 1,
   "png": "bench/tradeoff/om-20240401__0.0.png",
   "match": 99.9
@@ -3467,8 +3467,8 @@
  {
   "id": "om-20240401",
   "rho": 0.25,
-  "chars": 217,
-  "code": "<body bgcolor=0a6190><b style=\"position:fixed;inset:50px 55px 50px 55px;background:#eff8fe;clip-path:polygon(50% 0,220px 40%,271px 40%,100% 50%,271px 60%,220px 60%,50% 100%,70px 60%,19px 60%,0 50%,20px 40%,70px 40%)\">",
+  "chars": 207,
+  "code": "<body bgcolor=0a6190><b style=\"position:fixed;inset:50px 55px;background:#eff8fe;clip-path:polygon(50% 0,220px 40%,271px 40%,100% 50%,271px 60%,220px 60%,50% 100%,70px 60%,19px 60%,0 50%,20px 40%,70px 40%)\">",
   "shapes": 1,
   "png": "bench/tradeoff/om-20240401__0.25.png",
   "match": 99.9
@@ -3476,8 +3476,8 @@
  {
   "id": "om-20240401",
   "rho": 0.5,
-  "chars": 217,
-  "code": "<body bgcolor=0a6190><b style=\"position:fixed;inset:50px 55px 50px 55px;background:#eff8fe;clip-path:polygon(50% 0,220px 40%,271px 40%,100% 50%,271px 60%,220px 60%,50% 100%,70px 60%,19px 60%,0 50%,20px 40%,70px 40%)\">",
+  "chars": 207,
+  "code": "<body bgcolor=0a6190><b style=\"position:fixed;inset:50px 55px;background:#eff8fe;clip-path:polygon(50% 0,220px 40%,271px 40%,100% 50%,271px 60%,220px 60%,50% 100%,70px 60%,19px 60%,0 50%,20px 40%,70px 40%)\">",
   "shapes": 1,
   "png": "bench/tradeoff/om-20240401__0.5.png",
   "match": 99.9
@@ -3485,8 +3485,8 @@
  {
   "id": "om-20240401",
   "rho": 1.0,
-  "chars": 217,
-  "code": "<body bgcolor=0a6190><b style=\"position:fixed;inset:50px 55px 50px 55px;background:#eff8fe;clip-path:polygon(50% 0,220px 40%,271px 40%,100% 50%,271px 60%,220px 60%,50% 100%,70px 60%,19px 60%,0 50%,20px 40%,70px 40%)\">",
+  "chars": 207,
+  "code": "<body bgcolor=0a6190><b style=\"position:fixed;inset:50px 55px;background:#eff8fe;clip-path:polygon(50% 0,220px 40%,271px 40%,100% 50%,271px 60%,220px 60%,50% 100%,70px 60%,19px 60%,0 50%,20px 40%,70px 40%)\">",
   "shapes": 1,
   "png": "bench/tradeoff/om-20240401__1.0.png",
   "match": 99.9
@@ -3494,8 +3494,8 @@
  {
   "id": "om-20240401",
   "rho": 2.0,
-  "chars": 217,
-  "code": "<body bgcolor=0a6190><b style=\"position:fixed;inset:50px 55px 50px 55px;background:#eff8fe;clip-path:polygon(50% 0,220px 40%,271px 40%,100% 50%,271px 60%,220px 60%,50% 100%,70px 60%,19px 60%,0 50%,20px 40%,70px 40%)\">",
+  "chars": 207,
+  "code": "<body bgcolor=0a6190><b style=\"position:fixed;inset:50px 55px;background:#eff8fe;clip-path:polygon(50% 0,220px 40%,271px 40%,100% 50%,271px 60%,220px 60%,50% 100%,70px 60%,19px 60%,0 50%,20px 40%,70px 40%)\">",
   "shapes": 1,
   "png": "bench/tradeoff/om-20240401__2.0.png",
   "match": 99.9
@@ -3503,8 +3503,8 @@
  {
   "id": "om-20240401",
   "rho": 4.0,
-  "chars": 217,
-  "code": "<body bgcolor=0a6190><b style=\"position:fixed;inset:50px 55px 50px 55px;background:#eff8fe;clip-path:polygon(50% 0,220px 40%,271px 40%,100% 50%,271px 60%,220px 60%,50% 100%,70px 60%,19px 60%,0 50%,20px 40%,70px 40%)\">",
+  "chars": 207,
+  "code": "<body bgcolor=0a6190><b style=\"position:fixed;inset:50px 55px;background:#eff8fe;clip-path:polygon(50% 0,220px 40%,271px 40%,100% 50%,271px 60%,220px 60%,50% 100%,70px 60%,19px 60%,0 50%,20px 40%,70px 40%)\">",
   "shapes": 1,
   "png": "bench/tradeoff/om-20240401__4.0.png",
   "match": 99.9
@@ -3512,8 +3512,8 @@
  {
   "id": "om-20240402",
   "rho": 0.0,
-  "chars": 417,
-  "code": "<body bgcolor=90d3a6><b style=\"border-radius:0 50% 50% 0;box-shadow:0 170px #194e29\"><b style=\"left:95;border-radius:50% 0 0 50%;box-shadow:0 170px #194e29\"><b style=top:135;width:126><b style=left:0;top:135;width:125><b style=left:125;width:150;height:200><b style=\"left:155;top:80;height:140;background:#90d3a6;box-shadow:60px 0 #90d3a6\"><style>b{position:fixed;width:30;height:30;background:#194e29;left:275;top:50",
+  "chars": 414,
+  "code": "<body bgcolor=90d3a6><b style=\"left:275;border-radius:0 50% 50% 0;box-shadow:0 170px #194e29\"><b style=\"left:95;border-radius:50% 0 0 50%;box-shadow:0 170px #194e29\"><b style=\"left:0;top:135;padding:15 63;box-shadow:275px 0 #194e29\"><b style=\"left:125;padding:100 75\"><b style=\"left:155;top:80;padding:70 15;background:#90d3a6;box-shadow:60px 0 #90d3a6\"><style>b{position:fixed;padding:15;background:#194e29;top:50",
   "shapes": 9,
   "png": "bench/tradeoff/om-20240402__0.0.png",
   "match": 99.93
@@ -3521,8 +3521,8 @@
  {
   "id": "om-20240402",
   "rho": 0.25,
-  "chars": 417,
-  "code": "<body bgcolor=90d3a6><b style=\"border-radius:0 50% 50% 0;box-shadow:0 170px #194e29\"><b style=\"left:95;border-radius:50% 0 0 50%;box-shadow:0 170px #194e29\"><b style=top:135;width:126><b style=left:0;top:135;width:125><b style=left:125;width:150;height:200><b style=\"left:155;top:80;height:140;background:#90d3a6;box-shadow:60px 0 #90d3a6\"><style>b{position:fixed;width:30;height:30;background:#194e29;left:275;top:50",
+  "chars": 414,
+  "code": "<body bgcolor=90d3a6><b style=\"left:275;border-radius:0 50% 50% 0;box-shadow:0 170px #194e29\"><b style=\"left:95;border-radius:50% 0 0 50%;box-shadow:0 170px #194e29\"><b style=\"left:0;top:135;padding:15 63;box-shadow:275px 0 #194e29\"><b style=\"left:125;padding:100 75\"><b style=\"left:155;top:80;padding:70 15;background:#90d3a6;box-shadow:60px 0 #90d3a6\"><style>b{position:fixed;padding:15;background:#194e29;top:50",
   "shapes": 9,
   "png": "bench/tradeoff/om-20240402__0.25.png",
   "match": 99.93
@@ -3530,8 +3530,8 @@
  {
   "id": "om-20240402",
   "rho": 0.5,
-  "chars": 417,
-  "code": "<body bgcolor=90d3a6><b style=\"border-radius:0 50% 50% 0;box-shadow:0 170px #194e29\"><b style=\"left:95;border-radius:50% 0 0 50%;box-shadow:0 170px #194e29\"><b style=top:135;width:126><b style=left:0;top:135;width:125><b style=left:125;width:150;height:200><b style=\"left:155;top:80;height:140;background:#90d3a6;box-shadow:60px 0 #90d3a6\"><style>b{position:fixed;width:30;height:30;background:#194e29;left:275;top:50",
+  "chars": 414,
+  "code": "<body bgcolor=90d3a6><b style=\"left:275;border-radius:0 50% 50% 0;box-shadow:0 170px #194e29\"><b style=\"left:95;border-radius:50% 0 0 50%;box-shadow:0 170px #194e29\"><b style=\"left:0;top:135;padding:15 63;box-shadow:275px 0 #194e29\"><b style=\"left:125;padding:100 75\"><b style=\"left:155;top:80;padding:70 15;background:#90d3a6;box-shadow:60px 0 #90d3a6\"><style>b{position:fixed;padding:15;background:#194e29;top:50",
   "shapes": 9,
   "png": "bench/tradeoff/om-20240402__0.5.png",
   "match": 99.93
@@ -3539,8 +3539,8 @@
  {
   "id": "om-20240402",
   "rho": 1.0,
-  "chars": 417,
-  "code": "<body bgcolor=90d3a6><b style=\"border-radius:0 50% 50% 0;box-shadow:0 170px #194e29\"><b style=\"left:95;border-radius:50% 0 0 50%;box-shadow:0 170px #194e29\"><b style=top:135;width:126><b style=left:0;top:135;width:125><b style=left:125;width:150;height:200><b style=\"left:155;top:80;height:140;background:#90d3a6;box-shadow:60px 0 #90d3a6\"><style>b{position:fixed;width:30;height:30;background:#194e29;left:275;top:50",
+  "chars": 414,
+  "code": "<body bgcolor=90d3a6><b style=\"left:275;border-radius:0 50% 50% 0;box-shadow:0 170px #194e29\"><b style=\"left:95;border-radius:50% 0 0 50%;box-shadow:0 170px #194e29\"><b style=\"left:0;top:135;padding:15 63;box-shadow:275px 0 #194e29\"><b style=\"left:125;padding:100 75\"><b style=\"left:155;top:80;padding:70 15;background:#90d3a6;box-shadow:60px 0 #90d3a6\"><style>b{position:fixed;padding:15;background:#194e29;top:50",
   "shapes": 9,
   "png": "bench/tradeoff/om-20240402__1.0.png",
   "match": 99.93
@@ -3548,8 +3548,8 @@
  {
   "id": "om-20240402",
   "rho": 2.0,
-  "chars": 417,
-  "code": "<body bgcolor=90d3a6><b style=\"border-radius:0 50% 50% 0;box-shadow:0 170px #194e29\"><b style=\"left:95;border-radius:50% 0 0 50%;box-shadow:0 170px #194e29\"><b style=top:135;width:126><b style=left:0;top:135;width:125><b style=left:125;width:150;height:200><b style=\"left:155;top:80;height:140;background:#90d3a6;box-shadow:60px 0 #90d3a6\"><style>b{position:fixed;width:30;height:30;background:#194e29;left:275;top:50",
+  "chars": 414,
+  "code": "<body bgcolor=90d3a6><b style=\"left:275;border-radius:0 50% 50% 0;box-shadow:0 170px #194e29\"><b style=\"left:95;border-radius:50% 0 0 50%;box-shadow:0 170px #194e29\"><b style=\"left:0;top:135;padding:15 63;box-shadow:275px 0 #194e29\"><b style=\"left:125;padding:100 75\"><b style=\"left:155;top:80;padding:70 15;background:#90d3a6;box-shadow:60px 0 #90d3a6\"><style>b{position:fixed;padding:15;background:#194e29;top:50",
   "shapes": 9,
   "png": "bench/tradeoff/om-20240402__2.0.png",
   "match": 99.93
@@ -3557,8 +3557,8 @@
  {
   "id": "om-20240402",
   "rho": 4.0,
-  "chars": 417,
-  "code": "<body bgcolor=90d3a6><b style=\"border-radius:0 50% 50% 0;box-shadow:0 170px #194e29\"><b style=\"left:95;border-radius:50% 0 0 50%;box-shadow:0 170px #194e29\"><b style=top:135;width:126><b style=left:0;top:135;width:125><b style=left:125;width:150;height:200><b style=\"left:155;top:80;height:140;background:#90d3a6;box-shadow:60px 0 #90d3a6\"><style>b{position:fixed;width:30;height:30;background:#194e29;left:275;top:50",
+  "chars": 414,
+  "code": "<body bgcolor=90d3a6><b style=\"left:275;border-radius:0 50% 50% 0;box-shadow:0 170px #194e29\"><b style=\"left:95;border-radius:50% 0 0 50%;box-shadow:0 170px #194e29\"><b style=\"left:0;top:135;padding:15 63;box-shadow:275px 0 #194e29\"><b style=\"left:125;padding:100 75\"><b style=\"left:155;top:80;padding:70 15;background:#90d3a6;box-shadow:60px 0 #90d3a6\"><style>b{position:fixed;padding:15;background:#194e29;top:50",
   "shapes": 9,
   "png": "bench/tradeoff/om-20240402__4.0.png",
   "match": 99.93
diff --git a/docs/blog/index.html b/docs/blog/index.html
index 57c0685..771bc6f 100644
--- a/docs/blog/index.html
+++ b/docs/blog/index.html
@@ -99,9 +99,9 @@ <h2>A greedy search that can paint underneath</h2>
     <div><img src="img/om-20240222-o.png" alt="cssynth's rendering of the three rings" width="400" height="300"><span>cssynth, {{chars:om-20240222}} characters (human: {{human:om-20240222}})</span></div>
   </div>
 <pre>{{code:om-20240222}}</pre>
-  <figcaption>The last element is a dark disc: a spread shadow (<code>0 0 0 20px</code>) draws the top ring around it, and two more dark shadows punch the holes into the two light discs below.</figcaption>
+  <figcaption>The last element is a dark disc. Spread shadows (<code>0 0 0 20px</code>) draw the top ring around it and the right ring around a copy of it, and two dark shadows punch the holes into the lower rings.</figcaption>
 </figure>
-<p>Switching insertion at any depth off, so that new shapes always go on top, raises the total J over the benchmark by {{abl:top-only}}, one of the three largest effects of any single part.</p>
+<p>Switching insertion at any depth off, so that new shapes always go on top, raises the total J over the benchmark by {{abl:top-only}}, one of the largest effects of any single part.</p>
 
 <h2>Putting length into the search</h2>
 <p>A plain greedy search picks the shape that fixes the most pixels. That ignores what it costs to write: a polygon and a rounded box that fix about as many pixels look the same, though one is twice as long. So the search has a second mode, which picks the shape with the best</p>
@@ -132,7 +132,7 @@ <h2>Keeping the model honest</h2>
     <div><img src="img/om-20240301-t.png" alt="Target: a white sunburst with a central disc on sky blue" width="400" height="300"><span>target</span></div>
     <div><img src="img/om-20240301-o.png" alt="cssynth's rendering of the sunburst" width="400" height="300"><span>cssynth, {{chars:om-20240301}} characters (human: {{human:om-20240301}})</span></div>
   </div>
-  <figcaption>The background is a <code>linear-gradient</code> fitted by dynamic programming over every angle; turned copies of a rounded bar share one <code>&lt;u&gt;</code> rule; polygons that mirror each other share a <code>clip-path</code> and flip with <code>scale:-1</code>.</figcaption>
+  <figcaption>Four white wedges and a disc. The left and right wedges share the <code>clip-path</code> in the <code>&lt;i&gt;</code> rule, and one is the other flipped with <code>scale:-1</code>. Version 0.6.0 took 935 characters here, with a fitted gradient background and turned bars; re-searching the three best results found this program.</figcaption>
 </figure>
 
 <h2>What each part buys</h2>
@@ -147,17 +147,27 @@ <h2>Away from the benchmark</h2>
 <p>The held-out families found a blind spot. Three concentric circles need a filled disc first, and that disc on its own breaks every correct pixel inside it, so a search that adds one shape at a time never places it. A <em>nested-ring motif</em> fills a ring's hole, fits a shape to the filled region, recurses into the hole, and offers the whole stack as one step. Rings went from 5 of 8 solved to 7 of 8 on the held-out set. Over all 48, 43 are solved against 40, with less CPU time than v0.5.0 given 40% more search. On the tiny targets, cssynth's code is provably optimal on 33 of the 35 it reproduces exactly.</p>
 <p>Not everything transferred. Mirror and turn motifs made as many targets longer as shorter. A filter learned from the outcome of 989 proposals didn't recognize structures it hadn't been trained on. And petals stay weak, because Chromium smooths curved, turned edges differently from cssynth's model, so the right program looks wrong to the search. The details are in <a href="https://github.com/fMindstormer/cssynth/blob/main/docs/method.md#beyond-the-development-set">the method notes</a>.</p>
 
-<h2>Where it still loses</h2>
-<p>The greedy search commits to the shape that looks best now. A re-search pass takes the best result apart (one polygon at a time, then one color at a time), refills the gap and keeps whatever lowers J, which repairs some of those choices, but not the structural ones.</p>
+<h2>Comparing whole programs</h2>
+<p>The greedy search commits to the shape that looks best now. On this target, version 0.6.0 took one tall rounded box over both circles, because it fixes more pixels than either circle, and then patched the purple waist the box covered with slivers: 706 characters. Hill climbing can't repair that locally: start from one circle, and the climb grows it back into the tall box.</p>
 <figure>
   <div class="pair">
     <div><img src="img/om-20240227-t.png" alt="Target: two light circles stacked on a purple oval" width="400" height="300"><span>target</span></div>
     <div><img src="img/om-20240227-o.png" alt="cssynth's rendering of the flower" width="400" height="300"><span>cssynth, {{chars:om-20240227}} characters (human: {{human:om-20240227}})</span></div>
   </div>
 <pre>{{code:om-20240227}}</pre>
-  <figcaption>A person draws an oval and two circles. The search sees one tall rounded box that covers both circles at once, fixes more pixels than either circle, and takes it. Then it patches the purple waist the box covered with small slivers.</figcaption>
+  <figcaption>The light region comes apart into two parts where its distance to the edge narrows, so the re-search draws one shape per part, refills the rest and finishes the program. That program has the lower J, so it stays: the oval, one circle, and a <code>box-shadow</code> copy of it.</figcaption>
+</figure>
+<p>The re-search takes a result apart in several ways (one polygon at a time, one color at a time, one color redrawn as parts), refills the gap, finishes the program and keeps it when J drops. It runs on the three best results, not only the best, because a result that starts a little worse sometimes ends much better. Without the parts, the total J rises by {{abl:no-parts}}; re-searching only the best result, by {{abl:research-1}}.</p>
+
+<h2>Where it still loses</h2>
+<p>Some programs need a primitive cssynth doesn't have. The human solution draws this pin as one rounded element, clipped to a point, whose background is a three-band gradient: the light stripe. cssynth has no gradients on shapes, so it draws the stripe as its own box and the dark arcs as polygons, and comes out longer.</p>
+<figure>
+  <div class="pair">
+    <div><img src="img/om-20240326-t.png" alt="Target: a pink map pin with a light stripe down its rounded top" width="400" height="300"><span>target</span></div>
+    <div><img src="img/om-20240326-o.png" alt="cssynth's rendering of the pin" width="400" height="300"><span>cssynth, {{chars:om-20240326}} characters (human: {{human:om-20240326}})</span></div>
+  </div>
 </figure>
-<p>Hill climbing makes this hard to fix locally: start from one circle, and the climb grows it back into the tall box, because that fixes more pixels. What would find the human plan is a search that compares whole programs: two circles plus a cheap shadow against one box plus patches. That is the next step, along with gradients on shapes, borders and repeating a shape with <code>::before</code>/<code>::after</code>.</p>
+<p>Gradients on shapes are the next step, along with borders and repeating a shape with <code>::before</code>/<code>::after</code>. Choices that aren't parts of one color are also still made one step at a time.</p>
 
 <h2>Try it</h2>
 <p>The compiler is pure Python (NumPy and SciPy, with OR-Tools for the shadow grouping when it is installed), so it also runs in the browser. The <a href="../demo/">demo</a> loads it with Pyodide and compiles your picture locally. The source, the benchmark and a longer write-up of the method are <a href="https://github.com/fMindstormer/cssynth">on GitHub</a>.</p>
diff --git a/docs/method.md b/docs/method.md
index 5d74dde..42acc8e 100644
--- a/docs/method.md
+++ b/docs/method.md
@@ -196,17 +196,20 @@ wouldn't do the same. (The training figures here include the shorter
 `inset`, which the table above doesn't.)
 
 Measured in Chromium on Linux (the Benchmark workflow), against 0.6.0. J
-here counts Chromium's wrong pixels; these runs predate a fix that keeps a
-shared `padding` when `inset` would be shorter for one element, worth
-about 55 more characters:
+here counts Chromium's wrong pixels:
 
 | | Total J | Characters | ≥ 99% match | Shorter than human |
 |---|---|---|---|---|
 | 0.6.0 | 52,153 | 23,329 | 66 / 66 | 56 / 66 |
 | Parts, plus the shorter `inset` | 48,985 (−6.1%) | 21,817 | 66 / 66 | 59 / 66 |
 | The same, re-searching the 3 best | 47,179 (−9.5%) | 21,283 | 66 / 66 | 59 / 66 |
+| 0.7.0: the same, keeping a shared `padding` | 47,263 (−9.4%) | 21,175 | 66 / 66 | 60 / 66 |
 
-The lowest match stays at 99.21%. Re-searching the 3 best puts the lotus
+The last row keeps a `padding` that elements share when `inset` would be
+shorter for one of them. Code generation is never longer for the same
+shapes, but the clean-up reacts to the new lengths and ends elsewhere on a
+few targets, so the total J moved by +0.2% (108 fewer characters). The
+lowest match stays at 99.21%. Re-searching the 3 best puts the lotus
 under the human length and takes the wig over it (227 characters to 285,
 human: 264), for fewer wrong pixels.
 
@@ -295,25 +298,29 @@ add up):
 
 | Switched off | Total J | Characters | Shorter than human |
 |---|---|---|---|
-| nothing (full compiler) | 60,441 | 23,329 | 56 / 66 |
-| Insertion at any depth (shapes go on top) | 64,637 (+6.9%) | 24,887 (+1,558) | 54 / 66 |
-| Polygons traced from outlines | 64,449 (+6.6%) | 25,791 (+2,462) | 52 / 66 |
-| Re-search of the best result | 64,444 (+6.6%) | 24,652 (+1,323) | 55 / 66 |
-| Gradient backgrounds | 63,265 (+4.7%) | 24,077 (+748) | 54 / 66 |
-| Length-aware shape choice (by pixels only) | 62,921 (+4.1%) | 24,017 (+688) | 54 / 66 |
-| Length-aware clean-up | 61,982 (+2.5%) | 24,914 (+1,585) | 52 / 66 |
-| Splitting regions that touch at corners | 61,705 (+2.1%) | 24,005 (+676) | 55 / 66 |
-| Nested-ring motifs | 61,309 (+1.4%) | 23,495 (+166) | 56 / 66 |
-| Box-shadows | 61,258 (+1.4%) | 24,150 (+821) | 56 / 66 |
-| Soft gradient stops | 61,106 (+1.1%) | 23,680 (+351) | 55 / 66 |
-| Per-tag rules (`<i>`, `<u>`, `<s>`) | 60,807 (+0.6%) | 23,583 (+254) | 55 / 66 |
-| `clip-path` reuse for copies and mirrors | 60,708 (+0.4%) | 23,544 (+215) | 55 / 66 |
-| Exact (CP-SAT) shadow grouping, greedy kept | 60,554 (+0.2%) | 23,442 (+113) | 56 / 66 |
-
-The two ideas that shape the search, inserting shapes at any depth and
-re-searching the result, are worth the most; so are outline polygons, which
-replace stacks of slivers. Code-generation tricks are smaller on average,
-but each decides a few targets.
+| nothing (full compiler) | 55,925 | 21,175 | 60 / 66 |
+| Re-search of the best results | 63,980 (+14.4%) | 24,086 (+2,911) | 56 / 66 |
+| Redrawing a color in parts (re-search) | 59,350 (+6.1%) | 22,692 (+1,517) | 57 / 66 |
+| Polygons traced from outlines | 59,032 (+5.6%) | 23,074 (+1,899) | 59 / 66 |
+| Insertion at any depth (shapes go on top) | 58,545 (+4.7%) | 21,819 (+644) | 59 / 66 |
+| Length-aware shape choice (by pixels only) | 57,824 (+3.4%) | 21,386 (+211) | 61 / 66 |
+| Gradient backgrounds | 57,462 (+2.7%) | 21,514 (+339) | 59 / 66 |
+| Re-searching the 3 best results (only the best) | 57,269 (+2.4%) | 21,691 (+516) | 60 / 66 |
+| Nested-ring motifs | 56,758 (+1.5%) | 21,260 (+85) | 60 / 66 |
+| Box-shadows | 56,674 (+1.3%) | 21,876 (+701) | 60 / 66 |
+| Length-aware clean-up | 56,567 (+1.1%) | 21,779 (+604) | 61 / 66 |
+| `clip-path` reuse for copies and mirrors | 56,184 (+0.5%) | 21,254 (+79) | 59 / 66 |
+| Per-tag rules (`<i>`, `<u>`, `<s>`) | 56,171 (+0.4%) | 21,293 (+118) | 59 / 66 |
+| Splitting regions that touch at corners | 55,990 (+0.1%) | 21,158 (−17) | 61 / 66 |
+| Exact (CP-SAT) shadow grouping, greedy kept | 55,971 (+0.1%) | 21,221 (+46) | 60 / 66 |
+| Soft gradient stops | 55,961 (+0.1%) | 21,167 (−8) | 60 / 66 |
+
+Re-searching the results is worth the most, and redrawing a color in parts
+is 6.1% of that on its own; outline polygons, which replace stacks of
+slivers, and inserting shapes at any depth come next. Code-generation tricks
+are smaller on average. Splitting regions at corners, soft gradient stops
+and exact shadow grouping now move the total by 0.1% or less (in 0.6.0:
+2.1%, 1.1% and 0.2%).
 
 ## Experiment: learned start ranking
 
diff --git a/docs/pypi.md b/docs/pypi.md
index b80d955..b15f858 100644
--- a/docs/pypi.md
+++ b/docs/pypi.md
@@ -8,7 +8,7 @@ redraw in as few characters as they can.
 
 On the 66 targets it was developed on (its development set), rendered in
 Chromium, cssynth's code is shorter than the (minified, not golfed) human
-solution on 56, with all 66 at a 99%+ pixel match.
+solution on 60, with all 66 at a 99%+ pixel match.
 
 ```sh
 pip install cssynth

From 9b24f0a4fb9d178d18b682798aff819e53e0ea2e Mon Sep 17 00:00:00 2001
From: Fedir <98510937+fMindstormer@users.noreply.github.com>
Date: Tue, 10 Nov 2026 12:27:26 -0500
Subject: [PATCH 09/11] Results page: what changed in 0.7.0; twelve search
 settings, not eight

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 bench/build_page.py        | 8 ++++++--
 bench/cssynth-results.html | 8 ++++++--
 2 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/bench/build_page.py b/bench/build_page.py
index 3d9f59c..ffc5191 100755
--- a/bench/build_page.py
+++ b/bench/build_page.py
@@ -281,6 +281,10 @@ def main():
   <section aria-labelledby="h-new">
     <h2 id="h-new">What changed since the first version</h2>
     <div class="changes">
+      <article class="change">
+        <h3>Comparing whole programs</h3>
+        <p>The greedy search takes the shape that fixes the most pixels now, so two circles stacked on an oval became one tall box plus patches. The re-search now also splits a color's region where its distance to the edge comes apart, draws one shape per part, and keeps the finished program when its score is lower: that target went from 706 to 200 characters (human: 481). It also re-searches the three best results instead of only the best, because one that starts a little worse can end much better: the sunburst went from 935 to 460 characters. Over the benchmark, 60 targets come out shorter than the human code instead of 56, with 2,154 fewer characters.</p>
+      </article>
       <article class="change">
         <h3>Re-search and soft gradient stops</h3>
         <p>The greedy search commits to shapes in the order they first help. The best result is now taken apart and refilled, one polygon or one color at a time, and kept when the score drops; Pythagoras went from 330 to 240 characters. Chromium doesn't antialias hard gradient stops, so a flag whose bands were drawn with rotated boxes used to leave a band of wrong pixels along every edge. Each stop is now fitted to a sub-pixel position with a short ramp, which Chromium draws within 3/255 of the model: Tanzania became a single gradient, 602 &rarr; 142 characters (human: 336). A polygon that mirrors another now reuses its <code>clip-path</code> with <code>scale:-1 1</code>.</p>
@@ -353,8 +357,8 @@ def main():
       <li><b>Layered search.</b> Repeatedly add the shape that fixes the most wrong pixels: a box with any corner radii, an ellipse, a rotated bar or leaf, or a polygon clipped with <code>clip-path</code>, fitted to the region's convex hull or traced from its outline, concave or not. A new shape can go at any depth in the stack, not just on top. That is how rings, eyes and other nested figures get built.</li>
       <li><b>Refine.</b> Merge two pieces of the same color into one shape when that costs almost nothing, re-fit every shape against its final surroundings, drop shapes that no longer pay for their characters, and round coordinates where that changes no pixels.</li>
       <li><b>Code generation.</b> A shape that is a shifted, uniformly grown or shrunk copy of another becomes a <code>box-shadow</code> of it, when the paint order allows and CSS keeps the shape exact (a spread larger than the corner radius bends the corners). The grouping and the paint order are chosen together by an exact solver. Boxes are sized with unitless <code>padding</code> (allowed in quirks mode), saturated corner radii get short spellings like <code>7em</code>, and properties shared by every element move into one rule in a trailing, unclosed <code>&lt;style&gt;</code>; families of alike elements can take their own tag and rule.</li>
-      <li><b>Choose.</b> Eight search settings run per image (six searches, two of which also stop early at a higher threshold); each result is cleaned up for length and scored by characters + wrong pixels &divide; &rho;. The lowest score wins.</li>
-      <li><b>Re-search.</b> The winner is taken apart, one polygon or one color at a time, refilled by the same search, cleaned up again and kept when its score drops.</li>
+      <li><b>Choose.</b> Twelve search settings run per image (six searches, each choosing shapes by pixels alone or by length as well; two of the searches also stop early at a higher threshold); each result is cleaned up for length and scored by characters + wrong pixels &divide; &rho;.</li>
+      <li><b>Re-search.</b> The three best results are each taken apart, one polygon or one color at a time, or a color redrawn with one shape per part, refilled by the same search, cleaned up again and kept when the score drops. The lowest score wins.</li>
     </ol>
   </section>
 
diff --git a/bench/cssynth-results.html b/bench/cssynth-results.html
index a05a9d5..ca9f1ff 100644
--- a/bench/cssynth-results.html
+++ b/bench/cssynth-results.html
@@ -134,6 +134,10 @@ <h1>cssynth</h1>
   <section aria-labelledby="h-new">
     <h2 id="h-new">What changed since the first version</h2>
     <div class="changes">
+      <article class="change">
+        <h3>Comparing whole programs</h3>
+        <p>The greedy search takes the shape that fixes the most pixels now, so two circles stacked on an oval became one tall box plus patches. The re-search now also splits a color's region where its distance to the edge comes apart, draws one shape per part, and keeps the finished program when its score is lower: that target went from 706 to 200 characters (human: 481). It also re-searches the three best results instead of only the best, because one that starts a little worse can end much better: the sunburst went from 935 to 460 characters. Over the benchmark, 60 targets come out shorter than the human code instead of 56, with 2,154 fewer characters.</p>
+      </article>
       <article class="change">
         <h3>Re-search and soft gradient stops</h3>
         <p>The greedy search commits to shapes in the order they first help. The best result is now taken apart and refilled, one polygon or one color at a time, and kept when the score drops; Pythagoras went from 330 to 240 characters. Chromium doesn't antialias hard gradient stops, so a flag whose bands were drawn with rotated boxes used to leave a band of wrong pixels along every edge. Each stop is now fitted to a sub-pixel position with a short ramp, which Chromium draws within 3/255 of the model: Tanzania became a single gradient, 602 &rarr; 142 characters (human: 336). A polygon that mirrors another now reuses its <code>clip-path</code> with <code>scale:-1 1</code>.</p>
@@ -206,8 +210,8 @@ <h2 id="h-how">How it works</h2>
       <li><b>Layered search.</b> Repeatedly add the shape that fixes the most wrong pixels: a box with any corner radii, an ellipse, a rotated bar or leaf, or a polygon clipped with <code>clip-path</code>, fitted to the region's convex hull or traced from its outline, concave or not. A new shape can go at any depth in the stack, not just on top. That is how rings, eyes and other nested figures get built.</li>
       <li><b>Refine.</b> Merge two pieces of the same color into one shape when that costs almost nothing, re-fit every shape against its final surroundings, drop shapes that no longer pay for their characters, and round coordinates where that changes no pixels.</li>
       <li><b>Code generation.</b> A shape that is a shifted, uniformly grown or shrunk copy of another becomes a <code>box-shadow</code> of it, when the paint order allows and CSS keeps the shape exact (a spread larger than the corner radius bends the corners). The grouping and the paint order are chosen together by an exact solver. Boxes are sized with unitless <code>padding</code> (allowed in quirks mode), saturated corner radii get short spellings like <code>7em</code>, and properties shared by every element move into one rule in a trailing, unclosed <code>&lt;style&gt;</code>; families of alike elements can take their own tag and rule.</li>
-      <li><b>Choose.</b> Eight search settings run per image (six searches, two of which also stop early at a higher threshold); each result is cleaned up for length and scored by characters + wrong pixels &divide; &rho;. The lowest score wins.</li>
-      <li><b>Re-search.</b> The winner is taken apart, one polygon or one color at a time, refilled by the same search, cleaned up again and kept when its score drops.</li>
+      <li><b>Choose.</b> Twelve search settings run per image (six searches, each choosing shapes by pixels alone or by length as well; two of the searches also stop early at a higher threshold); each result is cleaned up for length and scored by characters + wrong pixels &divide; &rho;.</li>
+      <li><b>Re-search.</b> The three best results are each taken apart, one polygon or one color at a time, or a color redrawn with one shape per part, refilled by the same search, cleaned up again and kept when the score drops. The lowest score wins.</li>
     </ol>
   </section>
 

From 6217771e3ea94e633e61ca1cceddd2b121d9acda Mon Sep 17 00:00:00 2001
From: Fedir <98510937+fMindstormer@users.noreply.github.com>
Date: Tue, 10 Nov 2026 14:26:50 -0500
Subject: [PATCH 10/11] The fourth held-out set, spent on 0.7.0

Generated and compiled once in the Held-out workflow, 0.6.0's search, the
same with 48 starts per step (at least 0.7.0's CPU) and 0.7.0 one after
another on one runner. All three solve the same 43 of 48. On those, 0.7.0
writes 14,696 characters against 16,722 and 16,728 (-12.1%; 95% bootstrap
interval -22.7% to -1.4%), mostly on rings and petals, for 1.74 times the
CPU of 0.6.0's search (8,141 s against 4,667 s and 9,143 s). The
programs are committed as for the earlier sets; the pictures weren't
looked at.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 README.md                            |  32 ++--
 bench/heldout4/cases.json            | 242 +++++++++++++++++++++++++++
 bench/heldout4/mirror-20270121.html  |   1 +
 bench/heldout4/mirror-20270122.html  |   1 +
 bench/heldout4/mirror-20270123.html  |   1 +
 bench/heldout4/mirror-20270124.html  |   1 +
 bench/heldout4/mirror-20270125.html  |   1 +
 bench/heldout4/mirror-20270126.html  |   1 +
 bench/heldout4/mirror-20270127.html  |   1 +
 bench/heldout4/mirror-20270128.html  |   1 +
 bench/heldout4/petals-20270121.html  |   1 +
 bench/heldout4/petals-20270122.html  |   1 +
 bench/heldout4/petals-20270123.html  |   1 +
 bench/heldout4/petals-20270124.html  |   1 +
 bench/heldout4/petals-20270125.html  |   1 +
 bench/heldout4/petals-20270126.html  |   1 +
 bench/heldout4/petals-20270127.html  |   1 +
 bench/heldout4/petals-20270128.html  |   1 +
 bench/heldout4/repeat-20270121.html  |   1 +
 bench/heldout4/repeat-20270122.html  |   1 +
 bench/heldout4/repeat-20270123.html  |   1 +
 bench/heldout4/repeat-20270124.html  |   1 +
 bench/heldout4/repeat-20270125.html  |   1 +
 bench/heldout4/repeat-20270126.html  |   1 +
 bench/heldout4/repeat-20270127.html  |   1 +
 bench/heldout4/repeat-20270128.html  |   1 +
 bench/heldout4/rings-20270121.html   |   1 +
 bench/heldout4/rings-20270122.html   |   1 +
 bench/heldout4/rings-20270123.html   |   1 +
 bench/heldout4/rings-20270124.html   |   1 +
 bench/heldout4/rings-20270125.html   |   1 +
 bench/heldout4/rings-20270126.html   |   1 +
 bench/heldout4/rings-20270127.html   |   1 +
 bench/heldout4/rings-20270128.html   |   1 +
 bench/heldout4/scatter-20270121.html |   1 +
 bench/heldout4/scatter-20270122.html |   1 +
 bench/heldout4/scatter-20270123.html |   1 +
 bench/heldout4/scatter-20270124.html |   1 +
 bench/heldout4/scatter-20270125.html |   1 +
 bench/heldout4/scatter-20270126.html |   1 +
 bench/heldout4/scatter-20270127.html |   1 +
 bench/heldout4/scatter-20270128.html |   1 +
 bench/heldout4/stripes-20270121.html |   1 +
 bench/heldout4/stripes-20270122.html |   1 +
 bench/heldout4/stripes-20270123.html |   1 +
 bench/heldout4/stripes-20270124.html |   1 +
 bench/heldout4/stripes-20270125.html |   1 +
 bench/heldout4/stripes-20270126.html |   1 +
 bench/heldout4/stripes-20270127.html |   1 +
 bench/heldout4/stripes-20270128.html |   1 +
 cssynth/compiler.py                  |   5 +-
 docs/blog/index.html                 |   2 +-
 docs/method.md                       |  37 +++-
 53 files changed, 342 insertions(+), 24 deletions(-)
 create mode 100644 bench/heldout4/cases.json
 create mode 100644 bench/heldout4/mirror-20270121.html
 create mode 100644 bench/heldout4/mirror-20270122.html
 create mode 100644 bench/heldout4/mirror-20270123.html
 create mode 100644 bench/heldout4/mirror-20270124.html
 create mode 100644 bench/heldout4/mirror-20270125.html
 create mode 100644 bench/heldout4/mirror-20270126.html
 create mode 100644 bench/heldout4/mirror-20270127.html
 create mode 100644 bench/heldout4/mirror-20270128.html
 create mode 100644 bench/heldout4/petals-20270121.html
 create mode 100644 bench/heldout4/petals-20270122.html
 create mode 100644 bench/heldout4/petals-20270123.html
 create mode 100644 bench/heldout4/petals-20270124.html
 create mode 100644 bench/heldout4/petals-20270125.html
 create mode 100644 bench/heldout4/petals-20270126.html
 create mode 100644 bench/heldout4/petals-20270127.html
 create mode 100644 bench/heldout4/petals-20270128.html
 create mode 100644 bench/heldout4/repeat-20270121.html
 create mode 100644 bench/heldout4/repeat-20270122.html
 create mode 100644 bench/heldout4/repeat-20270123.html
 create mode 100644 bench/heldout4/repeat-20270124.html
 create mode 100644 bench/heldout4/repeat-20270125.html
 create mode 100644 bench/heldout4/repeat-20270126.html
 create mode 100644 bench/heldout4/repeat-20270127.html
 create mode 100644 bench/heldout4/repeat-20270128.html
 create mode 100644 bench/heldout4/rings-20270121.html
 create mode 100644 bench/heldout4/rings-20270122.html
 create mode 100644 bench/heldout4/rings-20270123.html
 create mode 100644 bench/heldout4/rings-20270124.html
 create mode 100644 bench/heldout4/rings-20270125.html
 create mode 100644 bench/heldout4/rings-20270126.html
 create mode 100644 bench/heldout4/rings-20270127.html
 create mode 100644 bench/heldout4/rings-20270128.html
 create mode 100644 bench/heldout4/scatter-20270121.html
 create mode 100644 bench/heldout4/scatter-20270122.html
 create mode 100644 bench/heldout4/scatter-20270123.html
 create mode 100644 bench/heldout4/scatter-20270124.html
 create mode 100644 bench/heldout4/scatter-20270125.html
 create mode 100644 bench/heldout4/scatter-20270126.html
 create mode 100644 bench/heldout4/scatter-20270127.html
 create mode 100644 bench/heldout4/scatter-20270128.html
 create mode 100644 bench/heldout4/stripes-20270121.html
 create mode 100644 bench/heldout4/stripes-20270122.html
 create mode 100644 bench/heldout4/stripes-20270123.html
 create mode 100644 bench/heldout4/stripes-20270124.html
 create mode 100644 bench/heldout4/stripes-20270125.html
 create mode 100644 bench/heldout4/stripes-20270126.html
 create mode 100644 bench/heldout4/stripes-20270127.html
 create mode 100644 bench/heldout4/stripes-20270128.html

diff --git a/README.md b/README.md
index 962adc2..adced0b 100644
--- a/README.md
+++ b/README.md
@@ -92,23 +92,25 @@ test it elsewhere (details in [docs/method.md](docs/method.md#beyond-the-develop
 **Held-out structural families.** 48 targets generated from six structures:
 mirrored shapes, repeats, concentric rings, petals, stripes and unrelated
 shapes. They are compiled once per version, never inspected, and scored in
-Chromium (`bench/evaluate.py`). The second baseline climbs 40% more starts,
-to spend at least the CPU time the motif search does:
+Chromium (`bench/evaluate.py`, run by the **Held-out** workflow). The fourth
+set was spent on 0.7.0, all three methods on one runner. The second baseline
+climbs 48 starts per step instead of 10, to spend at least the CPU time
+0.7.0 does:
 
-| | solved (≥ 99%) | characters, on the 40 all solve | CPU |
+| | solved (≥ 99%) | characters, on the 43 all solve | CPU |
 |---|---|---|---|
-| v0.5.0 | 40 / 48 | 11,944 | 2,634 s |
-| v0.5.0, 14 starts per step | 41 / 48 | **11,240** | 3,177 s |
-| with nested-ring motifs | **43 / 48** | 11,627 | **2,311 s** |
-
-The motifs solve the most with the least time. The baseline with more starts
-writes 3.4% less code on the targets all three solve, but that rests on three
-scatter and petals targets: it is shorter on 6 and longer on 7, and the 95%
-bootstrap interval runs from 2.1% shorter to 9.6% longer. Giving the default
-more starts makes its code longer, not shorter (+6.5% on the development
-split). (This is the third
-held-out set; the earlier two were spent on earlier versions, see
-[docs/method.md](docs/method.md#motifs).)
+| 0.6.0's search | 43 / 48 | 16,722 | **4,667 s** |
+| 0.6.0's search, 48 starts per step | 43 / 48 | 16,728 | 9,143 s |
+| 0.7.0 | 43 / 48 | **14,696** | 8,141 s |
+
+0.7.0 writes 12.1% less code on the targets all three solve: shorter than
+0.6.0's search on 9 of them and longer on 3, with a 95% bootstrap interval
+from 22.7% to 1.4% shorter. Rings (2,564 to 1,528 characters) and petals
+(4,333 to 3,292) gain the most; scatter is 70 characters longer. It solves
+no more targets and takes 1.7 times the CPU, and giving 0.6.0's search that
+time as more starts changes nothing (16,728 characters). (The earlier three
+sets were spent on earlier versions, see
+[docs/method.md](docs/method.md#beyond-the-development-set).)
 
 **Known optima.** On 40 tiny targets (up to four grid-aligned boxes and
 ellipses), an exact solver finds the shortest program that cssynth's own
diff --git a/bench/heldout4/cases.json b/bench/heldout4/cases.json
new file mode 100644
index 0000000..6037b0e
--- /dev/null
+++ b/bench/heldout4/cases.json
@@ -0,0 +1,242 @@
+[
+ {
+  "id": "mirror-20270121",
+  "family": "mirror",
+  "ref_len": 359
+ },
+ {
+  "id": "mirror-20270122",
+  "family": "mirror",
+  "ref_len": 227
+ },
+ {
+  "id": "mirror-20270123",
+  "family": "mirror",
+  "ref_len": 392
+ },
+ {
+  "id": "mirror-20270124",
+  "family": "mirror",
+  "ref_len": 269
+ },
+ {
+  "id": "mirror-20270125",
+  "family": "mirror",
+  "ref_len": 377
+ },
+ {
+  "id": "mirror-20270126",
+  "family": "mirror",
+  "ref_len": 363
+ },
+ {
+  "id": "mirror-20270127",
+  "family": "mirror",
+  "ref_len": 227
+ },
+ {
+  "id": "mirror-20270128",
+  "family": "mirror",
+  "ref_len": 382
+ },
+ {
+  "id": "repeat-20270121",
+  "family": "repeat",
+  "ref_len": 598
+ },
+ {
+  "id": "repeat-20270122",
+  "family": "repeat",
+  "ref_len": 1050
+ },
+ {
+  "id": "repeat-20270123",
+  "family": "repeat",
+  "ref_len": 611
+ },
+ {
+  "id": "repeat-20270124",
+  "family": "repeat",
+  "ref_len": 1791
+ },
+ {
+  "id": "repeat-20270125",
+  "family": "repeat",
+  "ref_len": 691
+ },
+ {
+  "id": "repeat-20270126",
+  "family": "repeat",
+  "ref_len": 584
+ },
+ {
+  "id": "repeat-20270127",
+  "family": "repeat",
+  "ref_len": 1061
+ },
+ {
+  "id": "repeat-20270128",
+  "family": "repeat",
+  "ref_len": 360
+ },
+ {
+  "id": "rings-20270121",
+  "family": "rings",
+  "ref_len": 378
+ },
+ {
+  "id": "rings-20270122",
+  "family": "rings",
+  "ref_len": 488
+ },
+ {
+  "id": "rings-20270123",
+  "family": "rings",
+  "ref_len": 596
+ },
+ {
+  "id": "rings-20270124",
+  "family": "rings",
+  "ref_len": 605
+ },
+ {
+  "id": "rings-20270125",
+  "family": "rings",
+  "ref_len": 507
+ },
+ {
+  "id": "rings-20270126",
+  "family": "rings",
+  "ref_len": 489
+ },
+ {
+  "id": "rings-20270127",
+  "family": "rings",
+  "ref_len": 379
+ },
+ {
+  "id": "rings-20270128",
+  "family": "rings",
+  "ref_len": 602
+ },
+ {
+  "id": "petals-20270121",
+  "family": "petals",
+  "ref_len": 935
+ },
+ {
+  "id": "petals-20270122",
+  "family": "petals",
+  "ref_len": 1158
+ },
+ {
+  "id": "petals-20270123",
+  "family": "petals",
+  "ref_len": 1047
+ },
+ {
+  "id": "petals-20270124",
+  "family": "petals",
+  "ref_len": 535
+ },
+ {
+  "id": "petals-20270125",
+  "family": "petals",
+  "ref_len": 1047
+ },
+ {
+  "id": "petals-20270126",
+  "family": "petals",
+  "ref_len": 662
+ },
+ {
+  "id": "petals-20270127",
+  "family": "petals",
+  "ref_len": 918
+ },
+ {
+  "id": "petals-20270128",
+  "family": "petals",
+  "ref_len": 408
+ },
+ {
+  "id": "stripes-20270121",
+  "family": "stripes",
+  "ref_len": 338
+ },
+ {
+  "id": "stripes-20270122",
+  "family": "stripes",
+  "ref_len": 337
+ },
+ {
+  "id": "stripes-20270123",
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new file mode 100644
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--- /dev/null
+++ b/bench/heldout4/repeat-20270126.html
@@ -0,0 +1 @@
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new file mode 100644
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+++ b/bench/heldout4/repeat-20270127.html
@@ -0,0 +1 @@
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new file mode 100644
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--- /dev/null
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@@ -0,0 +1 @@
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new file mode 100644
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@@ -0,0 +1 @@
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new file mode 100644
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@@ -0,0 +1 @@
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new file mode 100644
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--- /dev/null
+++ b/bench/heldout4/rings-20270123.html
@@ -0,0 +1 @@
+<body style="margin:0;background:#e8e7ce"><p style="position:fixed;left:210px;top:45px;width:120px;height:120px;background:#c55b3d;border-radius:50%"></p><p style="position:fixed;left:220px;top:55px;width:100px;height:100px;background:#222730;border-radius:50%"></p><p style="position:fixed;left:230px;top:65px;width:80px;height:80px;background:#e8e7ce;border-radius:50%"></p><p style="position:fixed;left:240px;top:75px;width:60px;height:60px;background:#c55b3d;border-radius:50%"></p><p style="position:fixed;left:250px;top:85px;width:40px;height:40px;background:#222730;border-radius:50%"></p>
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new file mode 100644
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--- /dev/null
+++ b/bench/heldout4/rings-20270124.html
@@ -0,0 +1 @@
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new file mode 100644
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+++ b/bench/heldout4/rings-20270125.html
@@ -0,0 +1 @@
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new file mode 100644
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@@ -0,0 +1 @@
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new file mode 100644
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--- /dev/null
+++ b/bench/heldout4/scatter-20270121.html
@@ -0,0 +1 @@
+<body style="margin:0;background:#ffe08f"><p style="position:fixed;left:140px;top:0px;width:160px;height:45px;background:#ff6a6a;border-radius:0 0 80px 80px;rotate:15deg"></p><p style="position:fixed;left:255px;top:20px;width:100px;height:50px;background:#191919;border-radius:10px"></p><p style="position:fixed;left:50px;top:75px;width:190px;height:45px;background:#ff6a6a;border-radius:50%"></p><p style="position:fixed;left:10px;top:70px;width:85px;height:75px;background:#191919;rotate:30deg"></p>
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new file mode 100644
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--- /dev/null
+++ b/bench/heldout4/scatter-20270122.html
@@ -0,0 +1 @@
+<body style="margin:0;background:#1a4341"><p style="position:fixed;left:45px;top:220px;width:115px;height:65px;background:#f3ac3c;border-radius:0 0 57px 57px;rotate:30deg"></p><p style="position:fixed;left:115px;top:135px;width:110px;height:160px;background:#998235;border-radius:45px;rotate:30deg"></p>
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new file mode 100644
index 0000000..cda6d2a
--- /dev/null
+++ b/bench/heldout4/scatter-20270123.html
@@ -0,0 +1 @@
+<body style="margin:0;background:#6592cf"><p style="position:fixed;left:320px;top:70px;width:65px;height:80px;background:#eeb850;border-radius:50%;rotate:15deg"></p><p style="position:fixed;left:180px;top:135px;width:30px;height:50px;background:#eeb850;border-radius:50%"></p><p style="position:fixed;left:45px;top:15px;width:85px;height:170px;background:#eeb850;border-radius:0 0 42px 42px"></p><p style="position:fixed;left:150px;top:115px;width:90px;height:125px;background:#0f2b34;border-radius:50%;rotate:15deg"></p>
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new file mode 100644
index 0000000..950287a
--- /dev/null
+++ b/bench/heldout4/scatter-20270124.html
@@ -0,0 +1 @@
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diff --git a/bench/heldout4/scatter-20270125.html b/bench/heldout4/scatter-20270125.html
new file mode 100644
index 0000000..53c84fe
--- /dev/null
+++ b/bench/heldout4/scatter-20270125.html
@@ -0,0 +1 @@
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new file mode 100644
index 0000000..110d8bc
--- /dev/null
+++ b/bench/heldout4/scatter-20270126.html
@@ -0,0 +1 @@
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new file mode 100644
index 0000000..ca5344d
--- /dev/null
+++ b/bench/heldout4/scatter-20270127.html
@@ -0,0 +1 @@
+<body style="margin:0;background:#ffffff"><p style="position:fixed;left:115px;top:45px;width:125px;height:75px;background:#222222;border-radius:0 0 62px 62px"></p><p style="position:fixed;left:180px;top:80px;width:165px;height:85px;background:#4f77ff;border-radius:15px;rotate:15deg"></p><p style="position:fixed;left:20px;top:80px;width:195px;height:50px;background:#4f77ff;rotate:30deg"></p><p style="position:fixed;left:15px;top:125px;width:200px;height:20px;background:#4f77ff;border-radius:10px"></p>
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new file mode 100644
index 0000000..d8f1dd7
--- /dev/null
+++ b/bench/heldout4/scatter-20270128.html
@@ -0,0 +1 @@
+<body style="margin:0;background:#b5e0ba"><p style="position:fixed;left:55px;top:10px;width:60px;height:105px;background:#ffffff;border-radius:50%;rotate:45deg"></p><p style="position:fixed;left:250px;top:40px;width:95px;height:165px;background:#ffffff;border-radius:25px;rotate:15deg"></p><p style="position:fixed;left:365px;top:165px;width:30px;height:125px;background:#5d3a3a;border-radius:50%;rotate:30deg"></p><p style="position:fixed;left:275px;top:45px;width:45px;height:135px;background:#ffffff;border-radius:45px 0"></p><p style="position:fixed;left:275px;top:110px;width:25px;height:125px;background:#ffffff;border-radius:50%;rotate:30deg"></p>
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diff --git a/bench/heldout4/stripes-20270121.html b/bench/heldout4/stripes-20270121.html
new file mode 100644
index 0000000..69392c5
--- /dev/null
+++ b/bench/heldout4/stripes-20270121.html
@@ -0,0 +1 @@
+<body style="margin:0;background:#998235"><p style="position:fixed;left:30px;top:0px;width:270px;height:300px;background:#1a4341"></p><p style="position:fixed;left:310px;top:0px;width:60px;height:300px;background:#1a4341"></p><p style="position:fixed;left:140px;top:90px;width:120px;height:120px;background:#f3ac3c;border-radius:50%"></p>
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diff --git a/bench/heldout4/stripes-20270122.html b/bench/heldout4/stripes-20270122.html
new file mode 100644
index 0000000..1fb0145
--- /dev/null
+++ b/bench/heldout4/stripes-20270122.html
@@ -0,0 +1 @@
+<body style="margin:0;background:#62374e"><p style="position:fixed;left:0px;top:20px;width:400px;height:150px;background:#e3516e"></p><p style="position:fixed;left:0px;top:210px;width:400px;height:10px;background:#e3516e"></p><p style="position:fixed;left:175px;top:125px;width:50px;height:50px;background:#fdc57b;border-radius:50%"></p>
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diff --git a/bench/heldout4/stripes-20270123.html b/bench/heldout4/stripes-20270123.html
new file mode 100644
index 0000000..220995f
--- /dev/null
+++ b/bench/heldout4/stripes-20270123.html
@@ -0,0 +1 @@
+<body style="margin:0;background:#ffffff"><p style="position:fixed;left:20px;top:0px;width:100px;height:300px;background:#5d3a3a"></p><p style="position:fixed;left:170px;top:0px;width:230px;height:300px;background:#5d3a3a"></p><p style="position:fixed;left:153px;top:103px;width:95px;height:95px;background:#b5e0ba;rotate:45deg"></p>
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diff --git a/bench/heldout4/stripes-20270124.html b/bench/heldout4/stripes-20270124.html
new file mode 100644
index 0000000..d279886
--- /dev/null
+++ b/bench/heldout4/stripes-20270124.html
@@ -0,0 +1 @@
+<body style="margin:0;background:#ffe08f"><p style="position:fixed;left:0px;top:20px;width:400px;height:110px;background:#ff6a6a"></p><p style="position:fixed;left:153px;top:103px;width:95px;height:95px;background:#191919;border-radius:50%"></p>
\ No newline at end of file
diff --git a/bench/heldout4/stripes-20270125.html b/bench/heldout4/stripes-20270125.html
new file mode 100644
index 0000000..8f04938
--- /dev/null
+++ b/bench/heldout4/stripes-20270125.html
@@ -0,0 +1 @@
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diff --git a/bench/heldout4/stripes-20270126.html b/bench/heldout4/stripes-20270126.html
new file mode 100644
index 0000000..a62d5ba
--- /dev/null
+++ b/bench/heldout4/stripes-20270126.html
@@ -0,0 +1 @@
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new file mode 100644
index 0000000..8b65277
--- /dev/null
+++ b/bench/heldout4/stripes-20270127.html
@@ -0,0 +1 @@
+<body style="margin:0;background:#ffffff"><p style="position:fixed;left:120px;top:0px;width:90px;height:300px;background:#b5e0ba"></p><p style="position:fixed;left:155px;top:105px;width:90px;height:90px;background:#5d3a3a;border-radius:50%"></p>
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diff --git a/bench/heldout4/stripes-20270128.html b/bench/heldout4/stripes-20270128.html
new file mode 100644
index 0000000..9137e1e
--- /dev/null
+++ b/bench/heldout4/stripes-20270128.html
@@ -0,0 +1 @@
+<body style="margin:0;background:#ffe08f"><p style="position:fixed;left:0px;top:110px;width:400px;height:180px;background:#191919"></p><p style="position:fixed;left:148px;top:98px;width:105px;height:105px;background:#ff6a6a;border-radius:50%"></p>
\ No newline at end of file
diff --git a/cssynth/compiler.py b/cssynth/compiler.py
index bc21df0..519a51d 100644
--- a/cssynth/compiler.py
+++ b/cssynth/compiler.py
@@ -37,8 +37,9 @@
 
 # How many of the best distinct results the re-search starts from (see
 # compile_image). On the benchmark, 3 instead of 1 lowered J by 3.7% in
-# Chromium for 1.5 to 2 times the CPU; with several processes they run in
-# parallel, so the wall time grows less.
+# Chromium, and the whole compile took 1.5 to 2.1 times the CPU (development
+# and training splits); with several processes they run in parallel, so the
+# wall time grows less.
 RESEARCH = 3
 
 
diff --git a/docs/blog/index.html b/docs/blog/index.html
index 771bc6f..2e65ec9 100644
--- a/docs/blog/index.html
+++ b/docs/blog/index.html
@@ -144,7 +144,7 @@ <h2>Results</h2>
 
 <h2>Away from the benchmark</h2>
 <p>Those 66 targets are where the compiler was developed, so they can't show how it generalizes. Two more benchmarks do. The first is 48 targets generated from six structures (mirrored shapes, repeats, concentric rings, petals, stripes, unrelated shapes), compiled once per version and never inspected. The second is tiny stacks of grid-aligned boxes and ellipses, where an exact solver finds the shortest program cssynth's own code generator can write.</p>
-<p>The held-out families found a blind spot. Three concentric circles need a filled disc first, and that disc on its own breaks every correct pixel inside it, so a search that adds one shape at a time never places it. A <em>nested-ring motif</em> fills a ring's hole, fits a shape to the filled region, recurses into the hole, and offers the whole stack as one step. Rings went from 5 of 8 solved to 7 of 8 on the held-out set. Over all 48, 43 are solved against 40, with less CPU time than v0.5.0 given 40% more search. On the tiny targets, cssynth's code is provably optimal on 33 of the 35 it reproduces exactly.</p>
+<p>The held-out families found a blind spot. Three concentric circles need a filled disc first, and that disc on its own breaks every correct pixel inside it, so a search that adds one shape at a time never places it. A <em>nested-ring motif</em> fills a ring's hole, fits a shape to the filled region, recurses into the hole, and offers the whole stack as one step. Rings went from 5 of 8 solved to 7 of 8 on the held-out set. Over all 48, 43 are solved against 40, with less CPU time than v0.5.0 given 40% more search. On a fourth held-out set, the whole-program re-search solves the same 43 targets as before and writes 12% less code, most of it on rings and petals, for 1.7 times the CPU; giving the previous version that time as more starts changes nothing. On the tiny targets, cssynth's code is provably optimal on 33 of the 35 it reproduces exactly.</p>
 <p>Not everything transferred. Mirror and turn motifs made as many targets longer as shorter. A filter learned from the outcome of 989 proposals didn't recognize structures it hadn't been trained on. And petals stay weak, because Chromium smooths curved, turned edges differently from cssynth's model, so the right program looks wrong to the search. The details are in <a href="https://github.com/fMindstormer/cssynth/blob/main/docs/method.md#beyond-the-development-set">the method notes</a>.</p>
 
 <h2>Comparing whole programs</h2>
diff --git a/docs/method.md b/docs/method.md
index 42acc8e..3c63348 100644
--- a/docs/method.md
+++ b/docs/method.md
@@ -188,9 +188,12 @@ several processes) and the best wins (`research=3`, the default):
 | Development (66) | 57,305 | 56,623 (−1.2%) | 55,994 (−2.3%) | 3.5× |
 | Training (48) | 70,284 | 70,222 (−0.1%) | 68,534 (−2.5%) | 5.8× |
 
-The whole compile takes 1.5 to 2 times the CPU, but little more wall time
-on 8 cores (the lotus: 16.9 s against 17.8 s). The lotus drops to 291
-characters (human: 418), from the second-best result. The winners come
+The whole compile takes 1.5 (development) to 2.1 (training) times the CPU,
+but little more wall time on 8 cores (the lotus: 16.9 s against 17.8 s).
+With the parts as well, 0.7.0 took 1.7 times the CPU of 0.6.0's search on
+the fourth held-out set, and 2.9 times on every third training target.
+The lotus drops to 291 characters (human: 418), from the second-best
+result. The winners come
 from many different variants, so re-searching one fixed extra variant
 wouldn't do the same. (The training figures here include the shorter
 `inset`, which the table above doesn't.)
@@ -366,7 +369,7 @@ from six structures: a shape and its mirror image (*mirror*), copies at a
 constant offset (*repeat*), concentric circles or squares (*rings*), leaves
 turned about a center (*petals*), bands with an emblem (*stripes*), and
 unrelated layered shapes as a control (*scatter*). A development split
-(seeds 0–7) was used to diagnose failures. Three held-out sets of 8 per
+(seeds 0–7) was used to diagnose failures. Four held-out sets of 8 per
 family, from fixed seeds, are committed as programs (`bench/heldout*/`).
 Each was compiled once per version under test and spent on one version (see
 [Motifs](#motifs)); their pictures were never looked at. The targets are
@@ -463,7 +466,8 @@ but on the 43 that the default also solves the code is 6.5% and 6.8% longer
 let a step win by more, and, as the learned proposals showed, a step that wins
 by more can still lead somewhere worse. More starts didn't shorten v0.5.0's
 code either: 2.4% longer on the development split and 0.8% longer on the 66
-targets. So there is no new version to spend a fourth held-out set on.
+targets. So there was no new version to spend a fourth held-out set on
+(it went to 0.7.0, below).
 
 **The held-out sets, honestly.** Each held-out set is compiled once per
 version, and its pictures are never looked at. The first was spent on the
@@ -479,7 +483,28 @@ characters against 9,994 (and 9,912), and 3,405 s against 3,029 s (and
 
 On the 66 development targets that version still lost 400 characters on two
 of them, so motifs moved to half the variants (above). The third set was
-spent on that, which is the shipped version.
+spent on that, which shipped as 0.6.0.
+
+The fourth set was spent on 0.7.0 (redrawing a color in parts, re-searching
+the three best results), in the Held-out workflow: the three methods ran one
+after another on one runner, rendered by the same Chromium as the
+development targets. The baseline at equal compute is 0.6.0's search with
+48 starts per step: on every third training target, 48 starts cost at least
+the CPU of 0.7.0 (each extra start about 33 s there), while 20 and 24 starts
+lowered nothing.
+
+| | solved | characters, on the 43 all solve | CPU |
+|---|---|---|---|
+| 0.6.0's search | 43 / 48 | 16,722 | 4,667 s |
+| 0.6.0's search, 48 starts | 43 / 48 | 16,728 | 9,143 s |
+| 0.7.0 | 43 / 48 | 14,696 (−12.1%) | 8,141 s |
+
+All three solve the same 43 targets. Against 0.6.0's search, 0.7.0 is
+shorter on 9 and longer on 3 (95% bootstrap interval for the total: 22.7%
+to 1.4% shorter); against the baseline with more starts, shorter on 12 and
+longer on 7 (23.1% to 1.5% shorter). Rings fall from 2,564 to 1,528
+characters and petals from 4,333 to 3,292; repeats and mirrors don't
+change, stripes lose 19 characters and scatter gains 70.
 
 **Rendering, again.** Chromium's edge on a turned straight side matches box
 filtering. On curved sides (rounded corners, leaves) the ramp has tails out

From a7500c1a50d1510b9b883a8ec09b56004eda7c68 Mon Sep 17 00:00:00 2001
From: Fedir <98510937+fMindstormer@users.noreply.github.com>
Date: Tue, 10 Nov 2026 14:29:17 -0500
Subject: [PATCH 11/11] Known optima for 0.7.0: unchanged, optimal on 33 of the
 35 exact

The shorter inset lowered five of the optima; cssynth still matches them.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 README.md       |   2 +-
 bench/tiny.json | 162 ++++++++++++++++++++++++------------------------
 2 files changed, 82 insertions(+), 82 deletions(-)

diff --git a/README.md b/README.md
index adced0b..14d6e13 100644
--- a/README.md
+++ b/README.md
@@ -115,7 +115,7 @@ sets were spent on earlier versions, see
 **Known optima.** On 40 tiny targets (up to four grid-aligned boxes and
 ellipses), an exact solver finds the shortest program that cssynth's own
 emitter can write. cssynth reproduces 35 exactly and is optimal on 33 of
-them (30 before this round); the other two are 40 characters over.
+them (30 before 0.6.0; the same in 0.7.0); the other two are 40 characters over.
 
 Petals (leaves turned about a center) remain the weak family: Chromium
 antialiases turned and curved edges differently from cssynth's model, so the
diff --git a/bench/tiny.json b/bench/tiny.json
index 98b2f38..4f3690d 100644
--- a/bench/tiny.json
+++ b/bench/tiny.json
@@ -15,8 +15,8 @@
   "cssynth_code": "<body bgcolor=1a4341><b style=\"inset:80px 230px 30px 10px;background:#e3516e\"><b style=\"inset:70px 240px 80px 70px;background:#62374e\"><b style=\"inset:10px 50px 180px 140px;background:#c55b3d\"><style>b{position:fixed;border-radius:50%",
   "cssynth_wrong": 0,
   "J_cssynth": 234.0,
-  "solve_s": 0.4,
-  "compile_s": 16.9
+  "solve_s": 0.0,
+  "compile_s": 14.5
  },
  {
   "seed": 7001,
@@ -32,8 +32,8 @@
   "cssynth_code": "<body bgcolor=6592cf><b style=\"position:fixed;inset:110px 120px 50px 40px;background:#e3516e\">",
   "cssynth_wrong": 0,
   "J_cssynth": 94.0,
-  "solve_s": 0.3,
-  "compile_s": 3.3
+  "solve_s": 0.0,
+  "compile_s": 2.9
  },
  {
   "seed": 7002,
@@ -52,8 +52,8 @@
   "cssynth_code": "<body bgcolor=e8e7ce><i style=\"inset:260px 180px 10px 170px;background:#1a4341\"><i style=\"inset:50px 90px 60px 180px;background:#998235\"><i style=\"inset:130px 140px 60px 120px;background:#fdc57b\"><b style=\"inset:20px 100px 120px 160px;background:#f7d716\"><style>b{position:fixed}i{position:fixed;border-radius:50%",
   "cssynth_wrong": 0,
   "J_cssynth": 313.0,
-  "solve_s": 0.5,
-  "compile_s": 37.6
+  "solve_s": 0.1,
+  "compile_s": 49.9
  },
  {
   "seed": 7003,
@@ -72,8 +72,8 @@
   "cssynth_code": "<body bgcolor=998235><b style=\"inset:140px 10px 80px 300px;background:#293462\"><b style=\"inset:180px 110px 0 40px;background:#f7d716\"><b style=\"inset:120px 120px 110px 150px;background:#fdc57b\"><b style=\"inset:210px 90px 30px 80px;background:#6592cf\"><style>b{position:fixed;border-radius:50%",
   "cssynth_wrong": 0,
   "J_cssynth": 292.0,
-  "solve_s": 0.5,
-  "compile_s": 14.4
+  "solve_s": 0.2,
+  "compile_s": 11.9
  },
  {
   "seed": 7004,
@@ -90,8 +90,8 @@
   "cssynth_code": "<body bgcolor=c55b3d><b style=left:220;top:20;padding:45;background:#998235><b style=\"inset:70px 150px 110px 10px;background:#62374e\"><style>b{position:fixed",
   "cssynth_wrong": 0,
   "J_cssynth": 157.0,
-  "solve_s": 0.3,
-  "compile_s": 5.5
+  "solve_s": 0.0,
+  "compile_s": 38.0
  },
  {
   "seed": 7005,
@@ -109,8 +109,8 @@
   "cssynth_code": "<body bgcolor=e8e7ce><b style=\"inset:30px 250px 100px 10px;background:#1a4341;box-shadow:105px 85px 0 -55px #fdc57b\"><b style=\"inset:40px 110px 180px 170px;background:#6592cf\"><style>b{position:fixed;border-radius:50%",
   "cssynth_wrong": 0,
   "J_cssynth": 217.0,
-  "solve_s": 0.4,
-  "compile_s": 10.5
+  "solve_s": 0.1,
+  "compile_s": 8.6
  },
  {
   "seed": 7006,
@@ -129,8 +129,8 @@
   "cssynth_code": "<body bgcolor=1a4341><b style=\"inset:220px 190px 50px 60px;background:#fdc57b\"><b style=\"inset:10px 80px 120px 90px;background:#e8e7ce\"><b style=\"inset:20px 200px 220px 0;background:#f3ac3c;border-radius:50%\"><b style=\"inset:80px 50px 0 260px;background:#998235\"><style>b{position:fixed",
   "cssynth_wrong": 0,
   "J_cssynth": 286.0,
-  "solve_s": 0.5,
-  "compile_s": 13.8
+  "solve_s": 0.1,
+  "compile_s": 44.6
  },
  {
   "seed": 7007,
@@ -149,8 +149,8 @@
   "cssynth_code": "<body bgcolor=e8e7ce><b style=\"inset:140px 140px 80px 230px\"><b style=\"inset:110px 170px 0 180px;background:#998235\"><b style=\"inset:70px 220px 10px 40px;background:#e3516e\"><b style=\"inset:140px 220px 80px 80px\"><b style=\"inset:180px 230px 0 0;background:#6592cf;border-radius:50%\"><style>b{position:fixed;background:#fdc57b",
   "cssynth_wrong": 0,
   "J_cssynth": 325.0,
-  "solve_s": 0.4,
-  "compile_s": 36.7
+  "solve_s": 0.1,
+  "compile_s": 46.6
  },
  {
   "seed": 7008,
@@ -168,8 +168,8 @@
   "cssynth_code": "<body bgcolor=6592cf><i style=\"inset:200px 170px 60px 150px;background:#e8e7ce\"><i style=\"inset:20px 130px 110px 30px;background:#fdc57b\"><b style=\"inset:90px 170px 80px 120px;background:#b5e0ba\"><style>b{position:fixed}i{position:fixed;border-radius:50%",
   "cssynth_wrong": 0,
   "J_cssynth": 254.0,
-  "solve_s": 0.4,
-  "compile_s": 10.8
+  "solve_s": 0.0,
+  "compile_s": 10.7
  },
  {
   "seed": 7009,
@@ -180,13 +180,13 @@
    "ellipse"
   ],
   "stacks": 1,
-  "optimal": 253,
-  "optimal_code": "<body bgcolor=293462><i style=\"inset:100px 120px 40px 250px;background:#6592cf\"><b style=left:220;top:100;padding:35;background:#c55b3d><i style=\"inset:100px 130px 30px 190px;background:#fdc57b\"><style>b{position:fixed}i{position:fixed;border-radius:50%",
-  "reference": 253,
-  "cssynth": 257,
-  "cssynth_code": "<body bgcolor=293462><i style=\"inset:100px 120px 40px 250px;background:#6592cf\"><b style=\"inset:100px 110px 130px 218px;background:#c55b3d\"><i style=\"inset:100px 130px 30px 190px;background:#fdc57b\"><style>b{position:fixed}i{position:fixed;border-radius:50%",
-  "cssynth_wrong": 7,
-  "J_cssynth": 271.0,
+  "optimal": 242,
+  "optimal_code": "<body bgcolor=293462><b style=\"left:250;padding:80 15;background:#6592cf;border-radius:50%\"><b style=left:220;padding:35;background:#c55b3d><b style=\"left:190;padding:85 40;background:#fdc57b;border-radius:50%\"><style>b{position:fixed;top:100",
+  "reference": 242,
+  "cssynth": 253,
+  "cssynth_code": "<body bgcolor=293462><b style=\"left:250;padding:80 15;background:#6592cf\"><b style=\"left:218;padding:35 36;background:#c55b3d;border-radius:4px 0 0 0\"><b style=\"left:190;padding:85 40;background:#fdc57b\"><style>b{position:fixed;border-radius:50%;top:100",
+  "cssynth_wrong": 4,
+  "J_cssynth": 261.0,
   "solve_s": 0.0,
   "compile_s": 9.0
  },
@@ -205,7 +205,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 109.0,
   "solve_s": 0.0,
-  "compile_s": 14.4
+  "compile_s": 21.9
  },
  {
   "seed": 7011,
@@ -224,7 +224,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 229.0,
   "solve_s": 0.1,
-  "compile_s": 7.9
+  "compile_s": 47.1
  },
  {
   "seed": 7012,
@@ -243,7 +243,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 250.0,
   "solve_s": 0.0,
-  "compile_s": 34.5
+  "compile_s": 142.0
  },
  {
   "seed": 7013,
@@ -260,7 +260,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 88.0,
   "solve_s": 0.0,
-  "compile_s": 6.6
+  "compile_s": 5.9
  },
  {
   "seed": 7014,
@@ -277,7 +277,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 113.0,
   "solve_s": 0.0,
-  "compile_s": 3.9
+  "compile_s": 3.5
  },
  {
   "seed": 7015,
@@ -295,7 +295,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 158.0,
   "solve_s": 0.0,
-  "compile_s": 4.6
+  "compile_s": 7.3
  },
  {
   "seed": 7016,
@@ -312,7 +312,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 90.0,
   "solve_s": 0.0,
-  "compile_s": 8.8
+  "compile_s": 8.9
  },
  {
   "seed": 7017,
@@ -331,8 +331,8 @@
   "cssynth_code": "<body bgcolor=f7d716><i style=\"inset:101px 219px 131px 40px;background:#f3ac3c\"><b style=\"inset:160px 300px 20px 50px;background:#c55b3d\"><i style=\"inset:0 50px 100px 160px;background:#6592cf\"><b style=\"inset:60px 120px 20px 100px;background:#293462\"><style>b{position:fixed}i{position:fixed;border-radius:50%",
   "cssynth_wrong": 16,
   "J_cssynth": 341.0,
-  "solve_s": 0.7,
-  "compile_s": 42.3
+  "solve_s": 0.6,
+  "compile_s": 60.8
  },
  {
   "seed": 7018,
@@ -350,7 +350,7 @@
   "cssynth_wrong": 12,
   "J_cssynth": 201.0,
   "solve_s": 0.0,
-  "compile_s": 27.9
+  "compile_s": 72.1
  },
  {
   "seed": 7019,
@@ -367,7 +367,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 112.0,
   "solve_s": 0.0,
-  "compile_s": 16.0
+  "compile_s": 23.5
  },
  {
   "seed": 7020,
@@ -384,7 +384,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 113.0,
   "solve_s": 0.0,
-  "compile_s": 3.6
+  "compile_s": 3.1
  },
  {
   "seed": 7021,
@@ -403,7 +403,7 @@
   "cssynth_wrong": 16,
   "J_cssynth": 284.0,
   "solve_s": 0.0,
-  "compile_s": 9.7
+  "compile_s": 18.9
  },
  {
   "seed": 7022,
@@ -420,7 +420,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 95.0,
   "solve_s": 0.0,
-  "compile_s": 3.4
+  "compile_s": 6.9
  },
  {
   "seed": 7023,
@@ -457,7 +457,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 175.0,
   "solve_s": 0.0,
-  "compile_s": 13.3
+  "compile_s": 22.0
  },
  {
   "seed": 7025,
@@ -469,15 +469,15 @@
    "ellipse"
   ],
   "stacks": 8,
-  "optimal": 311,
-  "optimal_code": "<body bgcolor=f3ac3c><b style=\"inset:0 250px 210px 100px;background:#c55b3d\"><b style=\"inset:60px 120px 100px 200px;background:#998235\"><i style=\"inset:80px 60px 190px 120px;background:#e3516e\"><i style=\"inset:130px 200px 80px 150px;background:#6592cf\"><style>b{position:fixed}i{position:fixed;border-radius:50%",
-  "reference": 311,
-  "cssynth": 311,
-  "cssynth_code": "<body bgcolor=f3ac3c><i style=\"inset:130px 200px 80px 150px;background:#6592cf\"><b style=\"inset:0 250px 210px 100px;background:#c55b3d\"><b style=\"inset:60px 120px 100px 200px;background:#998235\"><i style=\"inset:80px 60px 190px 120px;background:#e3516e\"><style>b{position:fixed}i{position:fixed;border-radius:50%",
+  "optimal": 303,
+  "optimal_code": "<body bgcolor=f3ac3c><b style=left:100;top:0;background:#c55b3d><b style=\"left:200;top:60;padding:70 40;background:#998235\"><b style=\"left:120;top:80;padding:15 110;background:#e3516e;border-radius:50%\"><b style=left:150;top:130;background:#6592cf;border-radius:50%><style>b{position:fixed;padding:45 25",
+  "reference": 303,
+  "cssynth": 303,
+  "cssynth_code": "<body bgcolor=f3ac3c><b style=left:150;top:130;background:#6592cf;border-radius:50%><b style=left:100;top:0;background:#c55b3d><b style=\"left:200;top:60;padding:70 40;background:#998235\"><b style=\"left:120;top:80;padding:15 110;background:#e3516e;border-radius:50%\"><style>b{position:fixed;padding:45 25",
   "cssynth_wrong": 0,
-  "J_cssynth": 311.0,
+  "J_cssynth": 303.0,
   "solve_s": 0.2,
-  "compile_s": 8.2
+  "compile_s": 7.7
  },
  {
   "seed": 7026,
@@ -497,7 +497,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 295.0,
   "solve_s": 0.1,
-  "compile_s": 6.5
+  "compile_s": 7.0
  },
  {
   "seed": 7027,
@@ -508,15 +508,15 @@
    "ellipse"
   ],
   "stacks": 1,
-  "optimal": 237,
-  "optimal_code": "<body bgcolor=e8e7ce><b style=\"inset:20px 250px 210px 50px;background:#6592cf\"><b style=\"inset:30px 210px 240px 130px;background:#1a4341\"><b style=\"inset:20px 260px 160px 70px;background:#293462\"><style>b{position:fixed;border-radius:50%",
-  "reference": 237,
-  "cssynth": 237,
-  "cssynth_code": "<body bgcolor=e8e7ce><b style=\"inset:20px 250px 210px 50px;background:#6592cf\"><b style=\"inset:30px 210px 240px 130px;background:#1a4341\"><b style=\"inset:20px 260px 160px 70px;background:#293462\"><style>b{position:fixed;border-radius:50%",
+  "optimal": 233,
+  "optimal_code": "<body bgcolor=e8e7ce><b style=\"left:50;padding:35 50;background:#6592cf\"><b style=\"left:130;top:30;padding:15 30;background:#1a4341\"><b style=\"left:70;padding:60 35;background:#293462\"><style>b{position:fixed;border-radius:50%;top:20",
+  "reference": 233,
+  "cssynth": 233,
+  "cssynth_code": "<body bgcolor=e8e7ce><b style=\"left:50;padding:35 50;background:#6592cf\"><b style=\"left:130;top:30;padding:15 30;background:#1a4341\"><b style=\"left:70;padding:60 35;background:#293462\"><style>b{position:fixed;border-radius:50%;top:20",
   "cssynth_wrong": 0,
-  "J_cssynth": 237.0,
+  "J_cssynth": 233.0,
   "solve_s": 0.0,
-  "compile_s": 8.2
+  "compile_s": 8.8
  },
  {
   "seed": 7028,
@@ -535,8 +535,8 @@
   "cssynth_code": "<body bgcolor=293462><b style=\"inset:210px 110px 60px 150px;background:#e3516e\"><i style=\"inset:20px 80px 200px 120px;background:#f3ac3c\"><i style=\"inset:0 70px 260px 220px;background:#6592cf\"><b style=\"inset:150px 170px 80px 20px;background:#62374e\"><style>b{position:fixed}i{position:fixed;border-radius:50%",
   "cssynth_wrong": 0,
   "J_cssynth": 309.0,
-  "solve_s": 0.2,
-  "compile_s": 9.0
+  "solve_s": 0.1,
+  "compile_s": 7.5
  },
  {
   "seed": 7029,
@@ -547,15 +547,15 @@
    "box"
   ],
   "stacks": 2,
-  "optimal": 239,
-  "optimal_code": "<body bgcolor=293462><b style=\"inset:30px 160px 60px 160px;background:#e8e7ce\"><b style=\"inset:120px 110px 140px 220px;background:#1a4341\"><b style=\"inset:30px 170px 220px 110px;background:#c55b3d;border-radius:50%\"><style>b{position:fixed",
-  "reference": 239,
-  "cssynth": 239,
-  "cssynth_code": "<body bgcolor=293462><b style=\"inset:30px 160px 60px 160px;background:#e8e7ce\"><b style=\"inset:120px 110px 140px 220px;background:#1a4341\"><b style=\"inset:30px 170px 220px 110px;background:#c55b3d;border-radius:50%\"><style>b{position:fixed",
+  "optimal": 233,
+  "optimal_code": "<body bgcolor=293462><b style=\"inset:30px 160px 60px;background:#e8e7ce\"><b style=\"inset:120px 110px 140px 220px;background:#1a4341\"><b style=\"inset:30px 170px 220px 110px;background:#c55b3d;border-radius:50%\"><style>b{position:fixed",
+  "reference": 233,
+  "cssynth": 233,
+  "cssynth_code": "<body bgcolor=293462><b style=\"inset:30px 160px 60px;background:#e8e7ce\"><b style=\"inset:120px 110px 140px 220px;background:#1a4341\"><b style=\"inset:30px 170px 220px 110px;background:#c55b3d;border-radius:50%\"><style>b{position:fixed",
   "cssynth_wrong": 0,
-  "J_cssynth": 239.0,
+  "J_cssynth": 233.0,
   "solve_s": 0.0,
-  "compile_s": 7.3
+  "compile_s": 11.6
  },
  {
   "seed": 7030,
@@ -573,7 +573,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 174.0,
   "solve_s": 0.0,
-  "compile_s": 8.0
+  "compile_s": 6.9
  },
  {
   "seed": 7031,
@@ -592,7 +592,7 @@
   "cssynth_wrong": 16,
   "J_cssynth": 282.0,
   "solve_s": 0.0,
-  "compile_s": 9.8
+  "compile_s": 9.2
  },
  {
   "seed": 7032,
@@ -611,7 +611,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 234.0,
   "solve_s": 0.0,
-  "compile_s": 36.6
+  "compile_s": 79.3
  },
  {
   "seed": 7033,
@@ -629,7 +629,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 155.0,
   "solve_s": 0.0,
-  "compile_s": 15.8
+  "compile_s": 30.3
  },
  {
   "seed": 7034,
@@ -648,8 +648,8 @@
   "cssynth_code": "<body bgcolor=c55b3d><i style=\"inset:20px 270px 70px 100px;background:#998235\"><b style=\"inset:90px 230px 50px 140px;background:#293462\"><i style=\"inset:90px 170px 180px 160px;background:#b5e0ba\"><i style=\"inset:120px 240px 80px 30px;background:#6592cf\"><style>b{position:fixed}i{position:fixed;border-radius:50%",
   "cssynth_wrong": 0,
   "J_cssynth": 312.0,
-  "solve_s": 0.2,
-  "compile_s": 8.2
+  "solve_s": 0.1,
+  "compile_s": 7.4
  },
  {
   "seed": 7035,
@@ -669,7 +669,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 350.0,
   "solve_s": 0.1,
-  "compile_s": 16.7
+  "compile_s": 28.7
  },
  {
   "seed": 7036,
@@ -688,7 +688,7 @@
   "cssynth_wrong": 0,
   "J_cssynth": 251.0,
   "solve_s": 0.0,
-  "compile_s": 17.6
+  "compile_s": 81.6
  },
  {
   "seed": 7037,
@@ -705,7 +705,7 @@
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   "J_cssynth": 112.0,
   "solve_s": 0.0,
-  "compile_s": 7.3
+  "compile_s": 7.9
  },
  {
   "seed": 7038,
@@ -716,15 +716,15 @@
    "box"
   ],
   "stacks": 2,
-  "optimal": 236,
-  "optimal_code": "<body bgcolor=1a4341><b style=\"inset:10px 150px 70px 150px;background:#e3516e;border-radius:50%\"><b style=\"inset:80px 40px 170px 220px;background:#293462\"><b style=\"inset:10px 190px 100px 80px;background:#c55b3d\"><style>b{position:fixed",
-  "reference": 236,
-  "cssynth": 236,
-  "cssynth_code": "<body bgcolor=1a4341><b style=\"inset:10px 150px 70px 150px;background:#e3516e;border-radius:50%\"><b style=\"inset:80px 40px 170px 220px;background:#293462\"><b style=\"inset:10px 190px 100px 80px;background:#c55b3d\"><style>b{position:fixed",
+  "optimal": 230,
+  "optimal_code": "<body bgcolor=1a4341><b style=\"inset:10px 150px 70px;background:#e3516e;border-radius:50%\"><b style=\"inset:80px 40px 170px 220px;background:#293462\"><b style=\"inset:10px 190px 100px 80px;background:#c55b3d\"><style>b{position:fixed",
+  "reference": 230,
+  "cssynth": 230,
+  "cssynth_code": "<body bgcolor=1a4341><b style=\"inset:10px 150px 70px;background:#e3516e;border-radius:50%\"><b style=\"inset:80px 40px 170px 220px;background:#293462\"><b style=\"inset:10px 190px 100px 80px;background:#c55b3d\"><style>b{position:fixed",
   "cssynth_wrong": 0,
-  "J_cssynth": 236.0,
+  "J_cssynth": 230.0,
   "solve_s": 0.0,
-  "compile_s": 21.0
+  "compile_s": 43.3
  },
  {
   "seed": 7039,
@@ -741,6 +741,6 @@
   "cssynth_wrong": 0,
   "J_cssynth": 94.0,
   "solve_s": 0.0,
-  "compile_s": 2.9
+  "compile_s": 2.7
  }
 ]
\ No newline at end of file